Factors Affecting Operative Time in Ureteroscopic Stone Removal: A Cross-Sectional Study for Prediction Model Construction

Why It Matters

Accurate time forecasts enable better operating‑room scheduling, improve patient safety, and optimize resource use in urological practice.

Key Takeaways

• Stone length lengthens ureteroscopic operative time
• Higher stone density predicts longer procedures
• Simple ureteral stones shorten surgery duration
• Prediction models show high discrimination (AUC 0.879)

Pulse Analysis

Ureteroscopic stone removal remains a cornerstone of modern urology, yet operative duration directly influences anesthesia risk, staff workload, and overall hospital throughput. Prolonged cases can increase complications such as infection or ureteral injury, making precise time estimation a critical component of surgical planning. By quantifying how specific stone characteristics affect procedure length, clinicians gain actionable insight that extends beyond generic scheduling heuristics.

The Shandong Provincial Third Hospital study leveraged a robust statistical pipeline, beginning with univariate screening and LASSO regularization to isolate fourteen candidate variables. Ultimately, stone length, maximum density, and the presence of a simple ureteral stone emerged as the most predictive factors. A Gamma regression model captured the continuous nature of operative time, while a logistic regression model dichotomized procedures for risk stratification. Internal validation via 1,000 bootstrap samples confirmed model stability, with the logistic model delivering an AUC of 0.879 and decision‑curve analysis confirming clinical utility.

For hospital administrators and urologists, these models translate into practical tools: a nomogram that estimates expected surgery length based on pre‑operative imaging and patient data. Implementing such predictions can streamline operating‑room allocation, reduce overtime costs, and enhance patient counseling regarding procedural risks. Moreover, the methodology sets a precedent for integrating machine‑learning techniques into other procedural specialties, encouraging data‑driven decision‑making across the healthcare continuum.