Ureteral endometriosis, the hidden enemy: multivariable fractional polynomial approach for evaluation of preoperative risk factors in the absence of ureteral dilation

Applying a multivariable fractional polynomial model, we evaluated the preoperative risk factors for ureteral involvement by endometriosis in women without hydronephrosis.

VOLUME 116, ISSUE 2, P470-477


Alessandro Arena, M.D., Simona Del Forno, M.D., Ph.D., Benedetta Orsini, M.D., Raffaella Iodice, M.D., Eugenia Degli Esposti, M.D., Anna Chiara Aru, M.D., Federica Manzara, M.D., Jacopo Lenzi, Ph.D., Diego Raimondo, M.D., Renato Seracchioli, M.D.



To determine whether it is possible to predict the risk of ureteral endometriosis (UE) using a mathematical model based on preoperative findings.


Prospective observational study conducted between January 2017 and April 2020.


Tertiary-level academic referral center.


Three hundred consecutive women of reproductive age with a diagnosis of posterior deep infiltrating endometriosis (DIE) scheduled for laparoscopic surgery.


Before surgery, anamnestic data and the severity of endometriosis-related symptoms were evaluated, and all patients underwent a complete gynecological examination. Transvaginal and transabdominal ultrasound were performed to map the endometriotic lesion. Ureteral involvement was surgically and histologically confirmed.

Main Outcome Measure(s)

To select important risk factors for UE and determine a suitable functional form for continuous predictors, we used the multivariable fractional polynomial.


UE was surgically found in 145 women (48.3%). Based on our multivariable polynomial mathematical model, UE was significantly associated with adenomyosis, parametrial involvement, and previous surgery for endometriosis. A posterior DIE nodule with a transverse diameter >1.8 cm was associated with a higher probability of ureteral involvement.


Posterior DIE nodule with a transverse diameter >1.8 cm, adenomyosis, parametrial involvement, and previous surgery for endometriosis appear to be good predictors of UE.