Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine

Abstract EN

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.