Using the weighted area under the net benefit curve for decision curve analysis
To predict outcome of patients with renal cell carcinoma (RCC) who undergo surgical therapy, risk models and nomograms are valuable tools. External validation on independent datasets is crucial for evaluating accuracy and generalizability of these models. The objective of the present study was to externally validate the postoperative nomogram developed by Karakiewicz et al. for prediction of cancer-specific survival. A total of 1,480 consecutive patients with a median follow-up of 82 months (IQR 46–128) were included into this analysis with 268 RCC-specific deaths. Nomogram-estimated survival probabilities were compared with survival probabilities of the actual cohort, and concordance indices were calculated. Calibration plots and decision curve analyses were used for evaluating calibration and clinical net benefit of the nomogram. Concordance between predictions of the nomogram and survival rates of the cohort was 0.911 after 12, 0.909 after 24 months and 0.896 after 60 months. Comparison of predicted probabilities and actual survival estimates with calibration plots showed an overestimation of tumor-specific survival based on nomogram predictions of high-risk patients, although calibration plots showed a reasonable calibration for probability ranges of interest. Decision curve analysis showed a positive net benefit of nomogram predictions for our patient cohort. The postoperative Karakiewicz nomogram provides a good concordance in this external cohort and is reasonably calibrated. It may overestimate tumor-specific survival in high-risk patients, which should be kept in mind when counseling patients. A positive net benefit of nomogram predictions was proven.