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Development and internal validation of a nomogram predicting 3-year chronic kidney disease upstaging following robot-assisted partial nephrectomy

  • Urology - Original Paper
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Abstract

Purpose

Aim of the present study was to develop and validate a nomogram to accurately predict the risk of chronic kidney disease (CKD) upstaging at 3 years in patients undergoing robot-assisted partial nephrectomy (RAPN).

Methods

A multi-institutional database was queried to identify patients treated with RAPN for localized renal tumor (cT1-cT2, cN0, cM0). Significant CKD upstaging (sCKD-upstaging) was defined as development of newly onset CKD stage 3a, 3b, and 4/5. Model accuracy was calculated according to Harrell C-index. Subsequently, internal validation using bootstrapping and calibration was performed. Then nomogram was depicted to graphically calculate the 3-year sCKD-upstaging risk. Finally, regression tree analysis identified potential cut-offs in nomogram-derived probability. Based on this cut-off, four risk classes were derived with Kaplan–Meier analysis tested this classification.

Results

Overall, 965 patients were identified. At Kaplan–Meier analysis, 3-year sCKD-upstaging rate was 21.4%. The model included baseline (estimated glomerular filtration rate) eGFR, solitary kidney status, multiple lesions, R.E.N.A.L. nephrometry score, clamping technique, and postoperative acute kidney injury (AKI). The model accurately predicted 3-year sCKD-upstaging (C-index 84%). Based on identified nomogram cut-offs (7 vs 16 vs 26%), a statistically significant increase in sCKD-upstaging rates between low vs intermediate favorable vs intermediate unfavorable vs high-risk patients (1.3 vs 9.2 vs 22 vs 54.2%, respectively, p < 0.001) was observed.

Conclusion

Herein we introduce a novel nomogram that can accurately predict the risk of sCKD-upstaging at 3 years. Based on this nomogram, it is possible to identify four risk categories. If externally validated, this nomogram may represent a useful tool to improve patient counseling and management.

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Data availability

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.

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Authors and Affiliations

  1. Department of Urology, IRCCS “Regina Elena” National Cancer Institute, Rome, Italy

    Rocco Simone Flammia, Umberto Anceschi, Gabriele Tuderti, Riccardo Mastroianni, Alfredo Bove, Costantino Leonardo & Giuseppe Simone

  2. Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy

    Fabrizio Di Maida, Antonio Andrea Grosso, Luca Lambertini, Andrea Mari & Andrea Minervini

  3. Unit of Oncologic Minimally-Invasive Urology and Andrology, Careggi Hospital, Florence, Italy

    Fabrizio Di Maida, Antonio Andrea Grosso, Luca Lambertini, Andrea Mari & Andrea Minervini

  4. Division of Experimental Oncology, Urological Research Institute (URI), IRCCS Ospedale San Raffaele, Milan, Italy

    Umberto Capitanio

  5. Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy

    Daniele Amparore, Cristian Fiori & Francesco Porpiglia

  6. Department of Urology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA

    Jennifer Lee & Daniel Eun

  7. Department of Urology, Rush University, Chicago, IL, USA

    Savio D. Pandolfo & Riccardo Autorino

Authors
  1. Rocco Simone Flammia
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  2. Umberto Anceschi
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  15. Andrea Minervini
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  19. Costantino Leonardo
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  20. Giuseppe Simone
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Correspondence to Rocco Simone Flammia.

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Flammia, R.S., Anceschi, U., Tuderti, G. et al. Development and internal validation of a nomogram predicting 3-year chronic kidney disease upstaging following robot-assisted partial nephrectomy. Int Urol Nephrol 56, 913–921 (2024). https://doi.org/10.1007/s11255-023-03832-6

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