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Background: The Deyo adaptation of the Charlson comorbidity index (DaCCI), which relies on 17 comorbid condition groupings, represents one of the most frequently used baseline comorbidity assessment tools in retrospective database studies. However, this index is not specific for patients with bladder cancer (BCa) treated with radical cystectomy (RC). The goal of this study was to develop a short-form of the original DaCCI (DaCCI-SF) that may specifically predict 90-day mortality after RC, with equal or better accuracy. Patients and Methods: Between 2000 and 2009, we identified 7,076 patients in the SEER-Medicare database with stage T1 through T4 nonmetastatic BCa treated with RC. We randomly divided the population into development (n=6,076) and validation (n=1,000) cohorts. Within the development cohort, logistic regression models tested the ability to predict 90-day mortality with various iterations of the DaCCI-SF, wherein <17 original comorbid condition groupings were included after adjusting for age, sex, race, T stage, and N stage. We relied on the Akaike information criterion to identify the most parsimonious and informative set of comorbid condition groupings. Accuracy of the DaCCI and the DaCCI-SF was tested in the external validation cohort. Results: Within the development cohort, the most parsimonious and informative model resulted in the inclusion of 3 of the 17 (17.6%) original comorbid condition groupings: congestive heart failure, cerebrovascular disease, and chronic pulmonary disease. Within the validation cohort, the accuracy was 68.4% for the DaCCI versus 69.7% for the DaCCI-SF. Higher accuracy of the DaCCI-SF was confirmed in subgroup analyses performed according to age (≤75 vs >75 years), stage (organ-confined vs non–organ-confined), type of diversion (ileal-conduit vs non–ileal-conduit), and treatment period. Conclusions: DaCCI-SF relies on 17.6% of the original comorbid condition groupings and provides higher accuracy for predicting 90-day mortality after RC compared with the original DaCCI, especially in most contemporary patients.

Background: Approximately 15% of the US population does not have health insurance. The objective of this study was to evaluate the impact of insurance status on tumor characteristics and treatment selection in patients with prostate cancer. Materials and Methods: We identified 20,393 patients younger than 65 years with prostate cancer in the 2010–2011 SEER database. Multivariable logistic regression analysis tested the relationship between insurance status and 2 end points: (1) presenting with low-risk prostate cancer at diagnosis and (2) receiving local treatment of the prostate. Locally weighted scatterplot smoothing methods were used to graphically explore the interaction among insurance status, use of local treatment, and baseline risk of cancer recurrence. The latter was defined using the Stephenson nomogram and CAPRA score. Results: Overall, 18,993 patients (93%) were insured, 849 (4.2%) had Medicaid coverage, and 551 (2.7%) were uninsured. At multivariable analysis, Medicaid coverage (odds ratio [OR], 0.67; 95% CI, 0.57, 0.80; P<.0001) and uninsured status (OR, 0.57; 95% CI, 0.46, 0.71; P<.0001) were independent predictors of a lower probability of presenting with low-risk disease. Likewise, Medicaid coverage (OR, 0.72; 95% CI, 0.60, 0.86; P=.0003) and uninsured status (OR, 0.45; 95% CI, 0.37, 0.55; P<.0001) were independent predictors of a lower probability of receiving local treatment. In uninsured patients, treatment disparities became more pronounced as the baseline cancer recurrence risk increased (10% in low-risk patients vs 20% in high-risk patients). Conclusions: Medicaid beneficiaries and uninsured patients are diagnosed with higher-risk disease and are undertreated. The latter is more accentuated for patients with high-risk prostate cancer. This may seriously compromise the survival of these individuals.