Cancer Medicine (Aug 2021)

Parallel comparison of R.E.N.A.L., PADUA, and C‐index scoring systems in predicting outcomes after partial nephrectomy: A systematic review and meta‐analysis

  • Can Hu,
  • Jiale Sun,
  • Zhiyu Zhang,
  • Haoyang Zhang,
  • Qi Zhou,
  • Jiangnan Xu,
  • Zhixin Ling,
  • Jun Ouyang

DOI
https://doi.org/10.1002/cam4.4047
Journal volume & issue
Vol. 10, no. 15
pp. 5062 – 5077

Abstract

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Abstract Objective To parallelly compare the applicability of the radius, exophytic/endophytic, nearness, anterior/posterior, location nephrometry score (R.E.N.A.L.), the Preoperative Aspects and Dimensions Used for an Anatomical (PADUA), and the centrality index (C‐index) scoring systems in predicting clinical outcomes after partial nephrectomy (PN). Methods We searched EMBASE, PubMed, Ovid, and Web of Science to perform a meta‐analysis examining the correlation coefficients between three nephrometry scores (NSs) and warm ischemia time (WIT), estimated blood loss (EBL), operation time (OT), length of stay (LOS), and absolute change in eGFR (ACE) up to 25 January 2021. Results In total, 13 studies including 1496 patients met the criteria for further analysis. Overall, all scoring systems had statistically significant correlations with the WIT, EBL, OT, ACE and LOS and ACE, except for the correlation between PADUA and LOS (r = 0.16 [−0.00, 0.31], p > 0.05). The C‐index had the strongest correlation with WIT (r = −0.35 [−0.43, −0.26], p < 0.05) and ACE (r = −0.29 [−0.48, −0.10], p < 0.05). Weak correlations were observed between OT as well as EBL and each scoring system. Publication bias was observed in PADUA score predicting ACE (p = 0.04) and high heterogeneity was found in some of our results. Conclusion Until now, this is the first meta‐analysis that parallelly compares these three scoring systems in predicting outcomes after PN. We found that all NSs showed a statistically significant correlation with WIT, EBL, OT, and ACE. Moreover, the C‐index scoring system is the best predictor of WIT and ACE. Due to the existence of publication bias and high heterogeneity, more well‐designed and large‐scale studies are warranted for validation.

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