Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
J Magn Reson Imaging. 2022 Oct;56(4):1145-1154. doi: 10.1002/jmri.28164. Epub 2022 Mar 18.
Cephalopelvic disproportion (CPD)-related obstructed labor is associated with maternal and neonatal morbidity and mortality. Accurate prediction of whether a primiparous woman is at high risk of an unplanned cesarean delivery would be a major advance in obstetrics.
To develop and validate a predictive model assessing the risk of cesarean delivery in primiparous women based on MRI findings.
Prospective.
A total of 150 primiparous women with clinical findings suggestive of CPD.
FIELD STRENGTH/SEQUENCE: T1-weighted fast spin-echo sequences, single-shot fast spin-echo (SSFSE) T2-weighted sequences at 1.5 T.
Pelvimetry and fetal biometry were assessed independently by two radiologists. A nomogram model combined that the clinical and MRI characteristics was constructed.
Univariable and multivariable logistic regression analyses were applied to select independent variables. Receiver operating characteristic (ROC) analysis was performed, and the discrimination of the model was assessed by the area under the curve (AUC). Calibration was assessed by calibration plots. Decision curve analysis was applied to evaluate the net clinical benefit. A P value below 0.05 was considered to be statistically significant.
In multivariable modeling, the maternal body mass index (BMI) before delivery, bilateral femoral head distance, obstetric conjugate, fetal head circumference, and fetal abdominal circumference was significantly associated with the likelihood of cesarean delivery. The discrimination calculated as the AUC was 0.838 (95% confidence interval [CI]: 0.774-0.902). The sensitivity and specificity of the nomogram model were 0.787 and 0.764, and the positive predictive and negative predictive values were 0.696 and 0.840, respectively. The model demonstrated satisfactory calibration (calibration slope = 0.945). Moreover, the decision curve analysis proved the superior net benefit of the model compared with each factor included.
Our study might provide a nomogram model that could identify primiparous women at risk of cesarean delivery caused by CPD based on MRI measurements.
2 TECHNICAL EFFICACY: Stage 2.
头盆不称（CPD）相关的梗阻性分娩与母婴发病率和死亡率有关。准确预测初产妇是否有计划外剖宫产的高风险将是产科的一大进步。
基于 MRI 结果，建立并验证一种评估初产妇剖宫产风险的预测模型。
前瞻性。
共有 150 例临床发现有 CPD 表现的初产妇。
磁场强度/序列：T1 加权快速自旋回波序列，1.5T 单激发快速自旋回波（SSFSE）T2 加权序列。
两位放射科医生独立评估骨盆测量和胎儿生物测量。通过列线图模型将临床和 MRI 特征相结合。
应用单变量和多变量逻辑回归分析选择独立变量。进行接收者操作特征（ROC）分析，通过曲线下面积（AUC）评估模型的区分度。通过校准图评估校准。应用决策曲线分析评估净临床获益。P 值低于 0.05 被认为具有统计学意义。
多变量建模中，分娩前母体体重指数（BMI）、双侧股骨头距离、产科入口前后径、胎头双顶径和胎腹围与剖宫产的可能性显著相关。AUC 计算的区分度为 0.838（95%置信区间[CI]：0.774-0.902）。该列线图模型的灵敏度和特异度分别为 0.787 和 0.764，阳性预测值和阴性预测值分别为 0.696 和 0.840。该模型显示出令人满意的校准（校准斜率=0.945）。此外，决策曲线分析证明该模型的净获益优于纳入的每个因素。
我们的研究可能为基于 MRI 测量提供一种列线图模型，用于识别因 CPD 导致剖宫产风险的初产妇。
2 级技术功效。
