A nomogram derived by combination of demographic and biomarker data improves the noninvasive evaluation of patients at risk for bladder cancer

Background: Improvements in the noninvasive clinical evaluation of patients at risk for bladder cancer would be of benefit both to individuals and to health care systems. We investigated the potential utility of a hybrid nomogram that combined key demographic features with the results of a multiplex urinary biomarker assay in hopes of identifying patients at risk of harboring bladder cancer. Methods: Logistic regression analysis was used to model the probability of bladder cancer burden in a cohort of 686 subjects (394 with bladder cancer) using key demographic features alone, biomarker data alone, and the combination of demographic features and key biomarker data. We examined discrimination, calibration, and decision curve analysis techniques to evaluate prediction model performance. Results: Area under the receiver operating characteristic curve (AUC) analyses revealed that demographic features alone predicted tumor burden with an accuracy of 0.806 [95% confidence interval (CI), 0.76-0.85], while biomarker data had an accuracy of 0.835 (95% CI, 0.80-0.87). The addition of molecular data into the nomogram improved the predictive performance to 0.891 (95% CI, 0.86-0.92). Decision curve analyses showed that the hybrid nomogram performed better than demographic or biomarker data alone. Conclusion: A nomogram construction strategy that combines key demographic features with biomarker data may facilitate the accurate, noninvasive evaluation of patients at risk of harboring bladder cancer. Further research is needed to evaluate the bladder cancer risk nomogram for potential clinical utility. Impact: The application of such a nomogram may better inform the decision to perform invasive diagnostic procedures.