Decision tree model predicts live birth after surgery for moderate-to-severe intrauterine adhesions

• Main Authors: Ru Zhu (Author), Hua Duan (Author), Wenbin Xu (Author), Sha Wang (Author), Lu Gan (Author), Qian Xu (Author), Jinjiao Li (Author)
• Format: Book
• Published: BMC, 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Abstract Background After treatment of intrauterine adhesions, the rate of re-adhesion is high and the pregnancy outcome unpredictable and unsatisfactory. This study established and verified a decision tree predictive model of live birth in patients after surgery for moderate-to-severe intrauterine adhesions (IUAs). Methods A retrospective observational study initially comprised 394 patients with moderate-to-severe IUAs diagnosed via hysteroscopy. The patients underwent hysteroscopic adhesiolysis from January 2013 to January 2017, in a university-affiliated hospital. Follow-ups to determine the rate of live birth were conducted by telephone for at least the first postoperative year. A classification and regression tree algorithm was applied to establish a decision tree model of live birth after surgery. Results Within the final population of 374 patients, the total live birth rate after treatment was 29.7%. The accuracy of the model was 83.8%, and the area under the receiver operating characteristic curve (AUC) was 0.870 (95% CI 7.699-0.989). The root node variable was postoperative menstrual pattern. The predictive accuracy of the multivariate logistic regression model was 70.3%, and the AUC was 0.835 (95% CI 0.667-0.962). Conclusions The decision tree predictive model is useful for predicting live birth after surgery for IUAs; postoperative menstrual pattern is a key factor in the model. This model will help clinicians make appropriate clinical decisions during patient consultations.

Item Description

10.1186/s12884-022-04375-x
1471-2393