Abstract
Objective To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design A retrospective study. Setting A tertiary hysteroscopic center at a teaching hospital. Population Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions Hysteroscopic adhesion separation surgery and second-look hysteroscopy 3 months later. Measurements and Main Results Patients' demographics, clinical indicators, and hysteroscopy data were collected from the electronic database of the hospital. The patients were randomly apportioned to either a training or testing set (332 and 142 patients, respectively). A decision tree model of adhesion recurrence was established with a classification and regression tree algorithm and validated with reference to a multivariate logistic regression model. The decision tree model was constructed based on the training set. The classification node variables were the risk factors for recurrence of IUAs: American Fertility Society score (root node variable), isolation barrier, endometrial thickness, tubal opening, uterine volume, and menstrual volume. The accuracies of the decision tree model and multivariate logistic regression analysis model were 75.35% and 76.06%, respectively, and areas under the receiver operating characteristic curve were 0.763 (95% CI 0.681–0.846) and 0.785 (95% CI 0.702–0.868). Conclusions The decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions.
Highlights
Intrauterine adhesions (IUAs) occur due to damage to the basal layer of the endometrium from various causes and disrupt the uterine anatomy [1]
Patients were retrospectively selected, who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs from January 2013 to December 2017 at Beijing Obstetrics and Gynecology Hospital Affiliated with Capital Medical University. e demographic, clinical history, imaging, and hysteroscopy evaluation data were collected. e center is a tertiary medical institution, in which almost 1000 patients receive hysteroscopy surgery annually
Included in this study were 576 patients who had moderate-to-severe IUAs within the previous five years (Figure 1). e 102 excluded patients comprised 4 and 6 patients due to older age and unsuccessful surgery, respectively; 28 patients lost to follow-up; and 64 with intrauterine or uterine lesions
Summary
To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions. A decision tree model of adhesion recurrence was established with a classification and regression tree algorithm and validated with reference to a multivariate logistic regression model. E decision tree model was constructed based on the training set. E accuracies of the decision tree model and multivariate logistic regression analysis model were 75.35% and 76.06%, respectively, and areas under the receiver operating characteristic curve were 0.763 (95% CI 0.681–0.846) and 0.785 (95% CI 0.702–0.868). E decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions Conclusions. e decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions
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