A Prospective Analysis of Thulium Fiber Laser Efficiency

Overview

Patients diagnosed with urolithiasis will be prospectively enrolled in this study. Preoperative diagnostic assessment will be performed using computed tomography (CT). Key parameters, including stone dimensions, quantity, total stone burden, and stone density (Hounsfield units), will be evaluated preoperatively. This study aims to analyze data from patients who undergo ureteroscopic Thulium Fiber Laser (TFL) lithotripsy and achieve a stone-free status within a single procedural session. Based on these data, a 'Specific Energy Coefficient' will be derived to enable the preoperative prediction of the total laser energy required for successful lithotripsy.

This prospective observational trial investigated the correlation between Thulium Fiber Laser (TFL) energy consumption and stone characteristics. Adult patients (≥18 years) with symptomatic urinary stones indicated for (f)URS and normal upper tract anatomy were included. Only patients achieving a complete stone-free status in a single session were analyzed. Exclusion criteria comprised active urinary tract infection, history of ipsilateral surgery or stent placement within six months, congenital renal anomalies, and postoperative residual fragments. Stone parameters were evaluated via non-contrast computed tomography (NCCT, 1.0 mm slice thickness). Mean stone density was measured in Hounsfield Units (HU) using a representative region of interest (ROI). Stone volume (V) was calculated using the validated ellipsoid formula. Procedures were performed by two high-volume endourologists using a 200 µm Thulium fiber. Semirigid URS (7.5-9.5 F) or flexible URS (7.5 F) with an access sheath (10/12-12/14 F) were utilized based on stone location. All cases employed "Dusting Mode" (0.1-0.2 J; 80-150 Hz). Laser ablation time was recorded from the initial firing until complete visual stone clearance/dusting. The primary outcome was the derivation of the Specific Energy Coefficient, calculated by dividing the total energy by the product of stone volume and density. Data were analyzed using SPSS v26.0. Normality was assessed via Shapiro-Wilk/Kolmogorov-Smirnov tests. Descriptive data were reported as mean ± standard deviation or median. Spearman's correlation and Multiple Linear Regression were used to identify independent predictors of laser energy requirements, with the model's predictive power reported as adjusted R2. Statistical significance was set at p \< 0.05.

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Outcomes