Nomogram for predicting survival in patients with unresectable and/or metastatic urothelial cancer who are treated with cisplatin-based chemotherapy

BACKGROUND The current study was conducted to develop a pretreatment prognostic model for patients with unresectable and/or metastatic urothelial cancer who were treated with first-line, cisplatin-based chemotherapy. METHODS Individual data were pooled from 399 patients who were enrolled on 8 phase 2 and 3 trials evaluating cisplatin-based, first-line chemotherapy in patients with metastatic urothelial carcinoma. Variables selected for inclusion in the model were combined in a Cox proportional hazards model to produce a points-based nomogram with which to predict the median, 1-year, 2-year, and 5-year survival. The nomogram was validated externally using data from a randomized trial of the combination of methotrexate, vinblastine, doxorubicin plus cisplatin versus docetaxel plus cisplatin. RESULTS The median survival of the development cohort was 13.8 months (95% confidence interval, 12.1 months-16.0 months); 68.2% of the patients had died at the time of last follow-up. On multivariable analysis, the number of visceral metastatic sites, Eastern Cooperative Oncology Group performance status, and leukocyte count were each found to be associated with overall survival (P <.05), whereas the site of the primary tumor and the presence of lymph node metastases were not. All 5 variables were included in the nomogram. When subjected to internal validation, the nomogram achieved a bootstrap-corrected concordance index of 0.626. When applied to the external validation cohort, the nomogram achieved a concordance index of 0.634. Calibration plots suggested that the nomogram was well calibrated for all predictions. CONCLUSIONS Based on routinely measured pretreatment variables, a nomogram was constructed that predicts survival in patients with unresectable and/or metastatic urothelial cancer who are treated with cisplatin-based chemotherapy. This model may be useful in patient counseling and clinical trial design.