Development of a Preoperative Difficulty Score for Retroperitoneal Laparoscopic Adrenalectomy: A Single-Center Retrospective Study.

Background

Retroperitoneoscopic laparoscopic adrenalectomy (RLA) is widely adopted for benign adrenal lesions due to its direct access and minimal bowel interference. However, expanding indications to larger tumors and complex anatomies increase technical difficulty because of the confined retroperitoneal space and obscured hilar landmarks. Current predictive models mainly focus on transperitoneal approaches and lack specificity for RLA. This study aimed to develop and internally validate a preoperative nomogram for predicting RLA-specific surgical difficulty.

Methods

All patients undergoing RLA from April 2024 to February 2025 at a single center were included. Operative time and postoperative hemoglobin (Hb) reduction were used as surrogate markers of difficulty. Patients were classified into high- and standard-risk groups. Univariate and multivariate analyses were performed to identify predictors of difficult RLA and develop a composite difficulty score.

Results

Tumor position (between the upper renal pole and renal pedicle: OR 8.819, 95% CI 3.981-21.462, p < 0.001) and pheochromocytoma (PHEO) pathology (OR 34.881, 95% CI 3.841-4719.428, p < 0.001) were the strongest independent predictors of high surgical difficulty, whereas tumor size (≥40 mm: OR 3.926, p = 0.071) showed limited predictive value. The composite score demonstrated excellent discrimination, with an optimism-corrected area under the curve (AUC) of 0.828 (95% CI 0.796-0.835). The nomogram showed robust calibration, and a cutoff score ≥ 2 yielded a negative predictive value of 0.940.

Conclusions

This study developed an RLA-specific nomogram incorporating tumor size, position, and pathology. The tumor-renal pedicle relationship was more predictive of surgical difficulty than tumor size alone, providing risk stratification to guide surgical planning, optimize patient selection, and enhance safety as RLA indications expand. External validation is required to confirm its generalizability.