EVALUATION OF FACTORS INFLUENCING SURGICAL OUTCOMES IN ROBOTIC LIVING DONOR NEPHRECTOMY BASED ON BAYESIAN ANALYSIS
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Abstract
Objective: Laparoscopic nephrectomy for living donors is widely regarded as the standard procedure for kidney transplantation in numerous medical centers worldwide. Robotic living donor nephrectomy (RDN) is progressively gaining recognition globally, assisting surgeons in overcoming the constraints associated with the conventional approach. This study aimed to report the outcomes of Robotic-assisted living donor nephrectomy and to analyze factors affecting surgical outcomes using Bayesian analysis.
Materials and Methods: All the donors who underwent RDN at Cho Ray Hospital from May 2018 to May 2025 were recruited to study. The donors were selected by the Renal Transplantation Council of Cho Ray Hospital based on national donor criteria. Demographic, clinical, and paraclinical characteristics, as well as surgical outcomes and complications, were recorded. Factors associated with surgical outcomes were analyzed using regression analysis, and Bayesian methods were applied to determine the optimal predictive model.
Results: A total of 47 living donors underwent RDN. The mean age was 46.6 ± 10.0 years (range: 27–65 years). The mean BMI was 23.2 ± 2.1 kg/m² (range: 22.6–28.9 kg/m²). Male donors accounted for 45.1% of cases, and left nephrectomy was performed in 87.2%. Complex renal vasculature was observed in 21.3% of donors. The mean operative time was 200.7 ± 41.1 minutes (range: 120–300 minutes), with a mean Robotic console time of 140.1 ± 43.2 minutes (range: 70–240 minutes). The mean estimated blood loss was 70.4 ± 40.1 mL (range: 10–200 mL). The postoperative complication rate was 4.2%. Bayesian analysis identified the optimal predictive model, showing that surgical outcomes were primarily influenced by two factors: the learning curve and donor body weight. Operative time decreased by 1.9 minutes with each additional accumulated surgical case, while it increased by 1.42 minutes for each kilogram increase in donor body weight after adjustment for the learning curve. These two factors explained 41.2% of the variability in operative time among cases.
Conclusion: Based on Bayesian analysis, these results suggest a clear learning curve effect and higher patient body weight was associated with slightly longer operative times, indicating increased surgical complexity in heavier patients.
Keywords
Kidney transplantation, laparoscopy, Robotic surgery, living donor nephrectomy
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References
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