The First Preoperative Risk Score for Postoperative Ileus Following Robotic Radical Prostatectomy: Novel Thresholds for BMI, HbA1c, and Tumor Risk Stratification

Postoperative Ileus

Abstract

Background: Postoperative ileus (POI) remains a common complication following robotic-assisted radical prostatectomy (RARP), delaying recovery and increasing healthcare burden. Obesity and impaired glycemic control are recognized contributors to poor postoperative outcomes through mechanisms such as inflammation, delayed gut motility, and impaired healing. However, their role in predicting POI, particularly in RARP, remains underexplored. This study aims to evaluate and define preoperative cutoff values for BMI and HbA1c associated with POI and develop the first risk score combining metabolic and oncological predictors.

Methods: A retrospective analysis was conducted on 200 patients who underwent RARP between January 2020 and December 2023. Preoperative variables included BMI, HbA1c, clinical T stage, PIRADS score, prostate volume (MRI), and biopsy Gleason score. Optimal thresholds for BMI and HbA1c were determined using ROC analysis and Youden’s J statistic. A six-point scoring system was developed based on categorical cutoffs, with logistic regression and ROC analysis used to evaluate performance.

Results: The overall incidence of POI was 17%. Novel thresholds—BMI ≥31.0 kg/m² and HbA1c ≥8.0%—were independently associated with higher POI risk. The additive score (range 0–6) showed progressive increases in POI incidence with higher scores. While the model demonstrated modest discrimination (AUC = 0.55), it remains clinically valuable given its simplicity and exclusive reliance on preoperative data.

Conclusion: This is the first study to define actionable preoperative thresholds for BMI and HbA1c in predicting POI after RARP. The resulting score offers a practical, bedside tool to support early risk stratification and preoperative counseling. Its simplicity supports clinical integration, and future multicenter validation may enhance predictive accuracy and expand its utility in enhanced recovery protocols.