Three-dimensional virtual models assistance predicts higher rates of "successful" minimally invasive partial nephrectomy: an Institutional analysis across the available trifecta definitions

Purpose: 3D virtual models (3DVMs) are nowadays under scrutiny to improve partial nephrectomy (PN) outcomes. Five different Trifecta definitions have been proposed to optimize the framing of “success” in the PN field. Our aim is to analyze if the use of 3DVMs could impact the success rate of minimally invasive PN (mi-PN), according to the currently available definitions of Trifecta. Materials and methods: At our Institution 250 cT1-2N0M0 renal masses patients treated with mi-PN were prospectively enrolled. Inclusion criteria were the availability of contrast-enhanced CT, baseline and postoperative serum creatinine, and eGFR. These patients were then compared with a control group of 710 patients who underwent mi-PN with the same renal function assessments, but without 3DVMs. Multivariable logistic regression (MLR) models were used to predict the trifecta achievement according to the different trifecta definitions. Results: Among the definitions, Trifecta rates ranged between 70.8% to 97.4% in the 3DVM group vs. 56.8% to 92.8% in the control group (all p values < 0.05). 3DVMs showed better postoperative outcomes in terms of ΔeGFR, (– 16.6% vs. – 2.7%, p = 0.03), postoperative complications (15%, vs 22.9%, p = 0.002) and major complications (Clavien Dindo > 3, 2.8% vs 5.6%, p = 0.03). At MLR 3DVMs assistance independently predicted higher rates of successful PN across all the available definitions of Trifecta (OR: 2.7 p < 0.001, OR:2.0 p = 0.0008, OR:2.8 p = 0.02, OR 2.0 p = 0.003). Conclusions: The 3DVMs availability was found to be the constant predictive factor of successful PN, with a twofold higher probability of achieving Trifecta regardless of the different definitions available in Literature.