Review: Use of nomograms for predictions of outcome in patients with advanced bladder cancer

Abstract

Introduction: Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with bladder cancer. In this review, we discuss the criteria for the evaluation of nomograms and review current available nomograms for advanced bladder cancer. Methods: A retrospective review of the Pubmed database between 2002 and 2008 was performed using the keywords `nomogram' and `bladder'. We limited the articles to advanced bladder cancer. We recorded input variables, prediction form, number of patients used to develop the prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed. Results: We discuss the characteristics needed to evaluate nomograms such as predictive accuracy, calibration, generalizability, level of complexity, effect of competing risks, conditional probabilities, and head-to-head comparison with other prediction methods. The predictive accuracies of the pre-cystectomy tools (n = 2) range from ~65—75% and that of the post-cystectomy tools (n = 5) range from ~75—80%. While some of these nomograms are well-calibrated and outperform AJCC staging, none has been externally validated. To date, four studies demonstrated a statistically significant improvement in predictive accuracy of nomograms by including biomarkers. Conclusions: Nomograms provide accurate individualized estimates of outcomes. They currently represent the most accurate and discriminatory decision-making aids tools for predicting outcomes in patients with bladder cancer. Use of current nomograms could improve current selection of patients for standard therapy and investigational trial design by ensuring homogeneous groups. The addition of biological markers to the currently available nomograms using clinical and pathologic data holds the promise of improving prediction and refining management of patients with bladder cancer.