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Title: [Establishment of Decision Tree Prediction Model for Risk Factors of Placenta Accreta Spectrum Disorders].
Author: Tan LS, Huang Y.
Journal: Sichuan Da Xue Xue Bao Yi Xue Ban; 2023 Mar; 54(2):400-405. PubMed ID: 36949705.
Abstract:
OBJECTIVE: 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. METHODS: 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: 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% CI: 5.24-12.22), the risk of PAS in multiple pregnancies was 2.52 times that in singleton pregnancies (95% CI: 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% CI: 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 in vitro 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. CONCLUSION: 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. 目的: 分析胎盘植入性疾病（placenta accreta spectrum disorders, PAS）的危险因素并初步构建PAS的决策树预测模型，旨在识别高危人群并为临床防治提供参考依据。方法: 通过查阅电子病历系统，回顾性分析成都市某医院中2020年1月–2020年9月分娩的2022例产妇的相关信息。采用单因素、多因素logistic回归方法对PAS的危险因素进行分析。运用SPSS Clementine12. 0软件初步探索PAS危险因素的决策树预测模型。结果: logistic回归提示PAS发生风险前三的是：本次妊娠合并前置胎盘的孕产妇发生PAS的风险是无前置胎盘产妇的8.00倍（95%CI：5.24～12.22）；多胎妊娠者发生PAS的风险是单胎妊娠者的2.52倍（95%CI：1.72～3.69）；三次及以上的人流史的孕产妇发生PAS的风险是未进行过人流手术者的1.89倍（95%CI：1.11～3.20）。C5.0决策树法预测模型结果：前置胎盘是最重要的危险因素，当合并有前置胎盘时，高达93.33%（140/150）患者发生 PAS；只存在体外受精-胚胎移植（in vitro fertilization-embryo transfer, IVF-ET）这一因素时，PAS的发生率达59.91%（133/222）；IVF-ET和子宫手术史同时存在时，PAS的发生率达75.96%（79/104）；既往有过人工流产的产妇发生PAS的概率达48.46%（205/423）；既往做过子宫手术的产妇发生PAS的概率为10.54%（37/351）；人工流产和子宫手术史同时存在时，PAS的发生率高达100%（163/163）。模型对训练集和测试集的预测准确率分别为85.41%和83.36%，准确率较高。结论: 通过决策树预测模型可快速、简便地筛选出PAS高危患者，进而积极动态评估 PAS的可能性并采取个体化的预防措施，避免不良结局的发生。

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