Recent scoring systems predicting stone-free status after retrograde intrarenal surgery; a systematic review and meta-analysis

• Published in: Central European journal of urology 2022, Vol.75 (1), p.72-80
• Main Authors: Özman, Oktay, Akgül, Hacı Murat, Başataç, Cem, Sancak, Eyüp Burak, Çınar, Önder, Çakır, Hakan, Yazıcı, Cenk Murat, Akpınar, Haluk, Önal, Bülent
• Format: Article
• Language: English
• Subjects: Bias; Keywords; Kidney stones; Localization; Meta-analysis; Metadata; Review Paper; Software; Success; Surgery; Systematic review
• ISSN: 2080-4806; 2080-4873; 2080-4873
• DOI: 10.5173/ceju.2022.0277

Several scoring systems and nomograms have been developed to predict the success of retrograde intrarenal surgery. But no meta-analysis for the performance of scoring systems has yet been performed. The aim of this study was to compare predictive ability of recent scoring systems for stone-free rate of retrograde intrarenal surgery. PubMed and Web of Science databases were searched systematically between April and May 2021. The scoring systems which were validated externally or studied at least by two different researcher groups were selected for further analysis. Of 59 records, 14 studies met the inclusion criteria (n = 4137). Area under curve (AUC) values of selected scoring systems were pooled in random or fixed effects. The I test was used to quantify heterogeneity. Eight, 5, 8, 4 and 3 studies included in meta-analyses for the modified Seoul National University Renal Stone Complexity Score (S-ReSC), R.I.R.S., Resorlu-Unsal Score (RUS), S.T.O.N.E., and Ito's Nomogram, respectively. We found pooled AUC values 0.709 (95% CI 0.670-0.748), 0.704 (95% CI 0.668-0.739), 0.669 (95% CI 0.646 to 0.692), and 0.771 (95% CI 0.724 to 0.818), for first four of them, respectively. Heterogeneity was very high to pool AUC values for Ito's nomogram. Although S.T.O.N.E. score showed higer pooled AUC value, this systematic review and meta-analysis has not revealed superiority of any scoring system. High heterogeneity between studies and dependencies between scoring systems make it difficult to design a comparative statistical model to generalize the findings. Also, limitations aside, neither scoring system has demonstrated good predictive/discriminative performance.