Purpose Level 1 evidence supports the use of neoadjuvant chemotherapy (NAC) for the treatment of muscle-invasive bladder cancer (MIBC), but observational data demonstrate that this approach is underused. A barrier to shared decision making is difficulty in predicting and communicating survival estimates after cystectomy with or without NAC. Methods We included patients with MIBC from the National Cancer Database treated with cystectomy. A state-transition model was constructed for calculating 5-year death risk using baseline patient-, tumor-, and facility-level variables. Internal-external cross-validation by geographic region was performed. The effect of NAC was integrated using a literature-derived hazard ratio. Bladder cancer-specific and other-cause mortality was estimated from all-cause mortality rates from US life tables. From the state-transition model, a Web-based tool was developed and pilot usability testing performed. Results A total of 9,824 patients with MIBC who underwent cystectomy were eligible for inclusion. Median overall survival was 39.6 months (95% CI, 37.4 to 42.4 months). Increasing age, higher clinical T stage, higher comorbidity index, and black race were associated with shorter survival. Private insurance, higher income, and cystectomy at a high-volume facility were associated with longer survival. The prediction model was well calibrated across geographic regions, with observed- to-predicted 5-year death risks ranging from 0.85 to 1.17. Absolute risk reductions with NAC varied from 8.6% to 10.1%. The Web-based tool allowed input of the predictor variables and a user-defined hazard ratio associated with the effect of NAC to generate individualized survival estimates. The tool demonstrated good usability with clinicians. Conclusion A Web-based tool was developed to individualize outcome prediction and communication in patients with MIBC treated with cystectomy with or without NAC to facilitate shared decision making.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalJCO clinical cancer informatics
Issue number1
StatePublished - 2017


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