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.