Meta-analysis and systematic review of factors predicting conversion to radical nephrectomy following robotic-assisted partial nephrectomy in renal cancer patients.

Abstract

Evaluating the risk factors for the conversion from robotic-assisted partial nephrectomy (RAPN) to radical nephrectomy (RN). Through a comprehensive database search encompassing PubMed, Web of Science, Embase, and the Cochrane Library, we identified pertinent English-language research published by June 2024. We utilized the NOS scale for quality assessment. The aggregate effect was quantified via the odds ratio (OR), alongside a 95% confidence interval (CI). Sensitivity analyses were conducted using both fixed-effects and random-effects models to evaluate reliability. The meta-analytical process was facilitated by the Stata 18 software suite. Our meta-analysis encompassed a total of 8 retrospective studies and 3 prospective studies, totaling 4056 patients. We found that increasing patient age (OR: 1.04; 95% CI 1.00-1.08; P = 0.005), higher American Society of Anesthesiologists (ASA) scores (3 or above) (OR: 2.74; 95% CI 1.52-4.93; P = 0.001), elevated R.E.N.A.L. scores (7 or above) (OR: 2.49; 95% CI 1.57-3.95; P < 0.001), and the use of off-clamp RAPN (OR: 7.21; 95% CI 2.60-19.93; P < 0.001) significantly raised the odds of surgical conversion. On the other hand, male sex (OR: 1.04; 95% CI 0.67-1.62; P = 0.858), the side of the tumor (OR: 0.97; 95% CI 0.48-1.95; P = 0.936), tumor size (OR: 3.43; 95% CI 0.57-20.55; P = 0.177), body mass index (BMI) (OR: 1.03; 95% CI 0.96-1.11; P = 0.426), clinical stage (OR: 3.78; 95% CI 0.46-30.70; P = 0.214), and the use of single-port RAPN (OR: 0.54; 95% CI 0.16-1.78; P = 0.31) did not show a statistically significant link to an increased conversion risk. This meta-analysis elucidates the critical risk factors for the conversion from robotic-assisted partial nephrectomy to radical nephrectomy, providing significant guidance for preoperative risk assessment and clinical decision-making. However, our findings necessitate validation through studies with larger sample sizes.