Validation of the deep pelvis endometriosis index (dPEI) to evaluate surgical outcomes of robotic-assisted surgery for endometriosis

The Deep Pelvic Endometriosis Index (dPEI) is a preoperative MRI-based score initially validated to predict surgical outcomes in patients undergoing laparoscopic treatment for deep pelvic endometriosis (DPE). Its applicability in robotic-assisted laparoscopy (RAL) has not yet been established. This study aimed to evaluate whether the dPEI can predict surgical outcomes following RAL for DPE. From February 2019 to December 2024, a retrospective analysis from a prospective database including patients undergoing RAL for DPE at Tenon Hospital, Paris, was performed. Preoperative staging was based on MRI and the dPEI scoring system, which evaluates the involvement of different anatomical compartments by deep endometriosis. Patients were classified into three categories: mild endometriosis (dPEI ≤ 2), moderate endometriosis (dPEI 3–4), and severe endometriosis (dPEI ≥ 5). Surgical outcomes including operative time, hospital stay, postoperative complications using the Clavien–Dindo classification and voiding dysfunction were assessed. A hundred and seventy patients were included. Overall complication rate was 24.7%, including 7.7% Clavien–Dindo grade > II. De novo voiding dysfunction occurred in 10.6% of patients, lasting > 1 month in 4.1%. dPEI categories showed a positive correlation with longer operative time (Spearman’s ρ = 0.40, p < 0.001) and increased hospital stay (Spearman’s ρ = 0.43, p II complications (OR = 13.1; 95% CI [1.54–111.3], p = 0.02) and high incidence of voiding dysfunction (OR = 5.9; 95% CI [1.48–23.5], p = 0.01). Involvement of lateral compartments was associated with high operative time, hospital stay, and de novo voiding dysfunction. Our results support the dPEI as a useful preoperative tool for predicting surgical outcomes after RAL for DPE. Its use can improve patient counseling, and shared decision-making, particularly in cases of severe disease (dPEI ≥ 5). Similar content being viewed by others Data availability The datasets analyzed during the current study are not publicly available due to patient privacy and institutional data protection policies at Tenon Hospital (Paris, France). However, anonymized data supporting the findings of this study are available from the corresponding author.

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