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<p>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. <b>This outcome occurred in 24 patients (14.7%) in the development cohort and 28 patients (22.0%) in the external validation cohort.</b> 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&#xa0;mm. Using the L-index and tumor volume, we developed the LIVED (<i>L</i>-<i>i</i>ndex and <i>v</i>olume for prediction <i>e</i>GFR <i>d</i>ecline) 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.</p>

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

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  • 发表日期：

    2026年3月10日

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  • Haruyuki Ohsugi,
  • Junichi Ikeda,
  • Kenta Takayasu,
  • Hisanori Taniguchi,
  • Masaaki Yanishi,
  • Hidefumi Kinoshita

摘要

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.