Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound

Abstract

We want to describe a model that allows the use of transperineal ultrasound to define the probability of experiencing uterine prolapse (UP). This was a prospective observational study involving 107 patients with UP or cervical elongation (CE) without UP. The ultrasound study was performed using transperineal ultrasound and evaluated the differences in the pubis–uterine fundus distance at rest and with the Valsalva maneuver. We generated different multivariate binary logistic regression models using nonautomated methods to predict UP, including the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver. The parameters were added progressively according to their simplicity of use and their predictive capacity for identifying UP. We used two binary logistic regression models to predict UP. Model 1 was based on the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient [AUC: 0.967 (95% CI, 0.939–0.995; p < 0.0005)]. Model 2 used the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver, age, avulsion and ballooning (AUC: 0.971 (95% CI, 0.945–0.997; p < 0.0005)). In conclusion, the model based on the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient could predict 96.7% of patients with UP.