Background and purpose: Several scoring systems have recently emerged to predict stone-free rate (SFR) and complications after percutaneous nephrolithotomy (PCNL). We aimed to compare the most commonly used scoring systems (Guy's stone score, S.T.O.N.E. nephrolithometry, and CROES nomogram), assess their predictive accuracy for SFR and other postoperative variables, and develop a risk group stratification based on these scoring systems.
Materials and methods: We performed a retrospective review of patients who have had a PCNL at four academic institutions between 2006 and 2013. Primary outcome was SFR within 3 weeks of the surgery and secondary outcomes were operative time (OT), complications, and length of stay (LOS). We performed chi-squared, t-test, logistic, linear, and Poisson regressions, as well as receiver operating characteristics curve with area under the curve (AUC) calculation.
Results: We identified 586 patients eligible for analysis. Of these, 67.4% were stone free. Guy's, S.T.O.N.E., and CROES score were predictive of SFR on multivariable logistic regression (odds ratio [OR]: 1.398, 95% confidence interval [CI]: 1.056, 1.852, p = 0.019; OR: 1.417, 85% CI: 1.231, 1.631, p < 0.001; OR: 0.993, 95% CI: 0.988, 0.998, p = 0.004) and have similar predictive accuracy with AUCs of 0.629, 0.671, and 0.646, respectively. On multivariable linear regression, only S.T.O.N.E. was an independent predictor of longer OT (β = 14.556, 95% CI: 12.453, 16.660, p < 0.001). None of the scores were independent predictors of postoperative complications or a longer LOS. Poisson regression allowed for risk group stratification and showed the S.T.O.N.E. score and CROES nomogram to have the most distinct risk groups.
Conclusions: The three evaluated scoring systems have similar predictive accuracy of SFR. S.T.O.N.E. has additional value in predicting OT. Risk group stratification can be used for patient counseling. Further research is needed to identify whether or not any is superior to the others with regard to clinical usefulness and predictive accuracy.