Tan Li-Shu, Huang Yan
Department of Nursing, West China Second University Hospital, Sichuan University, Chengdu 610041, China.
Key Laboratory of Birth Defects and Related Diseases of Women and Children of the Ministry of Education, Sichuan University, Chengdu 610041, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2023 Mar;54(2):400-405. doi: 10.12182/20230260307.
To analyze the risk factors for placenta accreta spectrum (PAS) disorders and to construct preliminarily a decision tree prediction model for PAS, to help identify high-risk populations, and to provide reference for clinical prevention and treatment.
By accessing the electronic medical record system, we retrospectively analyzed the relevant data of 2022 women who gave birth between January 2020 and September 2020 in a hospital in Chengdu. Univariate logistic regression and multivariate logistic regression were conducted to analyze the risk factors of PAS. SPSS Clementine12.0 was used to make preliminary exploration for the decision tree prediction model of PAS risk factors.
Results of logistic regression suggested that the top three risk factors for PAS included the following, the risk of PAS in pregnant women with placenta previa was 8.00 times that in pregnant women without placenta previa (95% : 5.24-12.22), the risk of PAS in multiple pregnancies was 2.52 times that in singleton pregnancies (95% : 1.72-3.69), and the risk of PAS in pregnant women who have had three or more abortions was 1.89 times that in those who have not had abortion (95% : 1.11-3.20). Results of the decision tree prediction model based on C5.0 algorithm were as follows, placenta previa was the most important risk factor, with as high as 93.33% (140/150) patients developed PAS when they had placenta previa; when fertilization-embryo transfer (IVF-ET) was the only factor the subjects had, the incidence of PAS was 59.91% (133/222); the incidence of PAS was as high as 75.96% (79/104) when the subjects had both IVF-ET and a history of uterine surgery; the probability of PAS in women who had induced abortion in the past was 48.46% (205/423); the probability of PAS in women who had undergone uterine surgery previously was 10.54% (37/351); the incidence of PAS was as high as 100.00% (163/163) when the subjects had induced abortion previously and uterine surgery history. The model showed a prediction accuracy of 85.41% for the training set and a prediction accuracy of 83.36% for the testing set, both being high rates of accuracy.
The decision tree prediction model can be used for rapid and easy screening of patients at high risk for PAS, so that the likelihood of PAS can be actively and dynamically assessed and individualized preventive measures can be taken to avoid adverse outcomes.
分析胎盘植入谱系疾病（PAS）的危险因素，并初步构建PAS的决策树预测模型，以帮助识别高危人群，为临床防治提供参考。
通过查阅电子病历系统，回顾性分析2020年1月至2020年9月在成都某医院分娩的2022例产妇的相关资料。采用单因素logistic回归和多因素logistic回归分析PAS的危险因素。运用SPSS Clementine12.0对PAS危险因素的决策树预测模型进行初步探索。
logistic回归结果显示，PAS的前三位危险因素如下，前置胎盘孕妇发生PAS的风险是无前置胎盘孕妇的8.00倍（95%：5.24-12.22），多胎妊娠发生PAS的风险是单胎妊娠的2.52倍（95%：1.72-3.69），有3次及以上流产史的孕妇发生PAS的风险是无流产史孕妇的1.89倍（95%：1.11-3.20）。基于C5.0算法的决策树预测模型结果如下，前置胎盘是最重要的危险因素，前置胎盘患者发生PAS的比例高达93.33%（140/150）；当受试者仅存在体外受精-胚胎移植（IVF-ET）这一因素时，PAS发生率为59.91%（133/222）；当受试者同时存在IVF-ET和子宫手术史时，PAS发生率高达75.96%（79/104）；既往有人工流产史的女性发生PAS的概率为48.46%（205/423）；既往有子宫手术史的女性发生PAS的概率为10.54%（37/351）；既往有人工流产史且有子宫手术史的受试者PAS发生率高达100.00%（163/163）。该模型对训练集的预测准确率为85.41%，对测试集的预测准确率为83.36%，均为较高的准确率。
决策树预测模型可用于快速简便地筛查PAS高危患者，从而能主动动态评估PAS发生的可能性，并采取个体化预防措施以避免不良结局。
