Xiao Xiaoyi, Feng Zhichao, Li Ting, Qiao Hong, Zhu Yun
Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, People's Republic of China.
Department of Medical Imaging, Yueyang Central Hospital, No. 39 Dongmaoling Road, Yueyang, 414020, Hunan, China.
Sci Rep. 2024 Dec 28;14(1):31378. doi: 10.1038/s41598-024-82894-7.
To develop and validate a nomogram for predicting the risk of adverse events (intraoperative massive haemorrhage or retained products of conception) associated with the termination of Caesarean scar pregnancy (CSP). Data were retrospectively collected from patients diagnosed with CSP who underwent Dilation and Curettage (D&C) at two hospitals. This data was divided into internal and external cohorts for analysis. The internal cohort was randomly split, with 70% of the data designated for a training set and 30% for an internal validation set. The external cohort served exclusively as the external validation set. LASSO and logistic regression techniques were employed to select variables and construct the nomogram. The performance of the nomogram was evaluated using various methods, including C-index, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA), to assess its identification, calibration, and clinical effectiveness. The prediction nomogram included several predictors, such as scar thickness, type of CSP, gestational sac diameter, and blood flow. It demonstrated strong discrimination, with a C-index of 0.83 (95% confidence interval: 0.77-0.89). Furthermore, in the internal validation set, a high C-index of 0.78 was achieved, while in the external validation set, it reached 0.83. Additional assessments using calibration curve analysis, DCA, and CICA indicated robust agreement between the nomogram's predictions and actual observations, highlighting its utility and reliability. The developed nomogram shows excellent discriminative ability and calibration, with the potential for effective local prediction of adverse events in CSP.
开发并验证一种列线图,用于预测与剖宫产瘢痕妊娠(CSP)终止相关的不良事件(术中大量出血或妊娠物残留)风险。回顾性收集两家医院诊断为CSP并接受刮宫术(D&C)的患者数据。这些数据被分为内部队列和外部队列进行分析。内部队列被随机分割,70%的数据指定用于训练集,30%用于内部验证集。外部队列仅作为外部验证集。采用LASSO和逻辑回归技术选择变量并构建列线图。使用包括C指数、校准曲线、决策曲线分析(DCA)和临床影响曲线分析(CICA)等多种方法评估列线图的性能,以评估其识别能力、校准情况和临床有效性。预测列线图包括几个预测因素,如瘢痕厚度、CSP类型、孕囊直径和血流情况。它显示出很强的区分能力,C指数为0.83(95%置信区间:0.77 - 0.89)。此外,在内部验证集中,C指数达到0.78,而在外部验证集中达到0.83。使用校准曲线分析、DCA和CICA进行的额外评估表明,列线图的预测与实际观察结果之间具有很强的一致性,突出了其效用和可靠性。所开发的列线图显示出优异的区分能力和校准情况,具有有效局部预测CSP不良事件的潜力。