Procedural factors outweigh anatomical morphometry in predicting postoperative pain following retrograde intrarenal...

Postoperative pain affects 23-45% of RIRS patients, but predictive factors remain unclear. This study evaluated whether anatomical morphometric assessment predicts postoperative pain when controlling for procedural variables in patients with normal upper tract anatomy, and identified which factors most significantly influence pain after RIRS.

This prospective single-center study included 420 consecutive patients undergoing RIRS (September 2024-May 2025). After exclusions, 320 patients completed three-week follow-up. Forty-five anatomical parameters and procedural variables (intrarenal pressure, laser parameters, access sheath use, complications) were recorded. Pain was assessed using VAS (seven timepoints) and Turkish USSQ. Analysis employed multiple comparison corrections, hierarchical regression, propensity score matching, and cross-validation.

Among 320 patients (67.8% male; mean age 51.6 ± 13.2 years), procedural factors primarily predicted postoperative pain. Intrarenal pressure (r = 0.448), complications (r = 0.418), and laser energy (r = 0.382) showed significant correlations with pain, accounting for most variance (R²=0.424). Anatomical parameters provided negligible predictive value (R²=-0.018), with no significant correlations after multiple testing correction. DJ stent pain was context-dependent, higher in high-pressure procedures but resolving rapidly post-removal in 89% of cases.

Modifiable procedural factors-intrarenal pressure, complications, and laser energy-are the main determinants of postoperative pain following RIRS in patients with normal anatomy, while anatomical measurements lack predictive value. Optimizing intraoperative practices offers greater clinical benefit than preoperative morphometric assessment. These findings support deprioritizing resource-intensive anatomical profiling for pain prediction in RIRS. Preoperative anatomical profiling should not be routinely performed for pain prediction, as procedural optimization represents modifiable targets with greater impact on patient outcomes.