A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data

Abstract

BACKGROUND: Cancer survival is related to features of the primary malignancy and concurrent presence of nonmalignant diseases (comorbidities), including weight‐related conditions (obesity, weight loss). The Charlson and Elixhauser methods are 2 well‐known methods that take comorbidities into account when explaining survival. They differ in both the number and categorization of comorbidities. METHODS: Cancer, comorbidity, and survival data were acquired from inpatient administrative hospital records in 574 colorectal cancer patients. Robust Poisson regression was used to analyze 2‐ and 3‐year survival according to cancer features and comorbidities classified by the Charlson and Elixhauser methods. Data for weight‐related conditions (body mass index, weight loss) and performance status were acquired upon a new patient visit to the regional cancer center. Discrimination was assessed with the concordance (c) statistic. RESULTS: A base model (age, sex, stage) had excellent discrimination (c‐statistic, 0.824 [2‐year survival] and 0.827 [3‐year survival]). The addition of Charlson comorbidities did not outperform the base model (c‐statistic, 0.831 [2‐year survival] and 0.833 [3‐year survival]). Elixhauser comorbidities added higher discrimination compared with the base model, both in stage and overall (c‐statistic, 0.852 [2‐year survival] and 0.854 [3‐year survival]; P < .01). The greatest increase in the c‐statistic contributed by the addition of the Elixhauser comorbidities occurred in stage II patients (increased from 0.683 to 0.838). Overall, the Elixhauser comorbidities outperformed the Charlson comorbidities (P < .05). The use of self‐reported weight and performance status data significantly increased discrimination by the Elixhauser method in 2‐year but not 3‐year survival. CONCLUSIONS: The Elixhauser method is a superior comorbidity risk‐adjustment model for colorectal cancer survival prediction. Cancer 2011. © 2010 American Cancer Society.