What question did this study set out to answer?

The study aims to develop a new classification using a location index to predict renal function after robotic partial nephrectomy.

March 13, 2026Scientific Reports0 citationsOpen Access

Development and validation of a risk classification integrating the location index to predict renal function after robotic partial nephrectomy

HOHaruyuki Ohsugi JIJunichi IkedaKansai Medical University KTKenta TakayasuKansai Medical University

Key Points

The study aims to develop a new classification using a location index to predict renal function after robotic partial nephrectomy.
Calculated a location index (L-index) to predict renal function post-surgery.
Validated the L-index in two cohorts, including 163 patients for development and 127 for external validation.
Defined a primary endpoint as a 20% or greater reduction in estimated glomerular filtration rate (eGFR) after surgery.
Developed the LIVED classification based on L-index and tumor volume.
Evaluated prediction accuracy using area under the receiver operating characteristic curve (AUC).
The L-index cutoff values were established at ≤ 15 and ≤ 30 mm.
14.7% of patients in the development cohort and 22.0% in the validation cohort experienced significant eGFR decline.
The LIVED classification achieved an AUC of 0.858, outperforming other nephrometry scoring systems.

Abstract

Nephrometry scoring systems are used to predict the surgical complexity of partial nephrectomy (PN) but are insufficient to predict renal function after robotic PN (RPN). The study aimed to calculate a new location factor for predicting postoperative renal function and develop a classification including the location factor. We calculated a location index (L-index) and verified an optimal cutoff value to predict renal function after RPN in 163 patients (development cohort). Then, we developed a new classification with the L-index and validated it in 127 patients (external validation cohort). The primary endpoint was an estimated glomerular filtration rate (eGFR) reduction of ≥ 20% from baseline to 6 months after RPN. This outcome occurred in 24 patients (14.7%) in the development cohort and 28 patients (22.0%) in the external validation cohort. The accuracy for predicting the endpoint was evaluated using area under the receiver operating characteristic curve (AUC). The L-index cutoff values were ≤ 15 and ≤ 30 mm. Using the L-index and tumor volume, we developed the LIVED (L-index and volume for prediction eGFR decline) classification dividing patients into three groups. The classification showed a high AUC compared to other nephrometry scoring systems (AUC = 0.858 vs. 0.674–0.744) in a validation cohort. The LIVED classification integrating the L-index, quantified as a location factor, and tumor volume predicted renal function after RPN with high accuracy.

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