Purpose: To develop a prognostic model to predict 30-day mortality following colorectal cancer (CRC) surgery using the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked data and to assess whether race/ethnicity, neighborhood, and hospital characteristics influence model performance.
Methods: We included patients aged 66 years and older from the linked 2000-2005 SEER-Medicare database. Outcome included 30-day mortality, both in-hospital and following discharge. Potential prognostic factors included tumor, treatment, sociodemographic, hospital, and neighborhood characteristics (census-tract-poverty rate). We performed a multilevel logistic regression analysis to account for nesting of CRC patients within hospitals. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) for discrimination and the Hosmer-Lemeshow goodness-of-fit test for calibration.
Results: In a model that included all prognostic factors, important predictors of 30-day mortality included age at diagnosis, cancer stage, and mode of presentation. Race/ethnicity, census-tract-poverty rate, and hospital characteristics were independently associated with 30-day mortality, but they did not influence model performance. Our SEER-Medicare model achieved moderate discrimination (AUC = 0.76), despite suboptimal calibration.
Conclusions: We developed a prognostic model that included tumor, treatment, sociodemographic, hospital, and neighborhood predictors. Race/ethnicity, neighborhood, and hospital characteristics did not improve model performance compared with previously developed models.