BACKGROUND: Comorbidity is an important consideration in oncology practice, particularly among older patients. Although a variety of comorbidity indices have been employed in research studies, it is unclear whether any one index is preferred. METHODS: An age-stratified random sample of 345 men (mean age of 69 years) who were newly diagnosed with prostate cancer were identified from a cancer registry in Ontario, Canada. Comorbidity and treatment information were obtained from chart review. Four comorbidity indices were utilized: Charlson Index, Diagnosis Count, Index of Coexistent Disease (ICED), and number of medications. Logistic regression analysis was used to compare the performance of comorbidity measures with respect to predicting receipt of curative treatment (radical prostatectomy or radiotherapy) and overall 6-year survival. Multivariable model performance including each of the comorbidity measures was compared by calculating the area under the receiver operating characteristic curve (AUROC). RESULTS: Among men with localized disease (n = 231), in models adjusted for age, Gleason score, and prostate-specific antigen level, only the Charlson Index was found to be a statistically significant predictor of receipt of curative treatment (P < .05), although all comorbidity indices had similar AUROC in adjusted models. After a median follow-up of 6.5 years, 116 of 345 men (33.6%) had died. In adjusted models, all 4 comorbidity indices performed similarly in predicting overall survival. CONCLUSIONS: Although comorbidity is an important predictor of both curative treatment and overall survival in prostate cancer, the optimal comorbidity index for use in research remains unclear. Selecting the optimal comorbidity index may depend on both the specific patient population and the outcome being considered.
BACKGROUND: Comorbidity is an important consideration in oncology practice, particularly among older patients. Although a variety of comorbidity indices have been employed in research studies, it is unclear whether any one index is preferred. METHODS: An age-stratified random sample of 345 men (mean age of 69 years) who were newly diagnosed with prostate cancer were identified from a cancer registry in Ontario, Canada. Comorbidity and treatment information were obtained from chart review. Four comorbidity indices were utilized: Charlson Index, Diagnosis Count, Index of Coexistent Disease (ICED), and number of medications. Logistic regression analysis was used to compare the performance of comorbidity measures with respect to predicting receipt of curative treatment (radical prostatectomy or radiotherapy) and overall 6-year survival. Multivariable model performance including each of the comorbidity measures was compared by calculating the area under the receiver operating characteristic curve (AUROC). RESULTS: Among men with localized disease (n = 231), in models adjusted for age, Gleason score, and prostate-specific antigen level, only the Charlson Index was found to be a statistically significant predictor of receipt of curative treatment (P < .05), although all comorbidity indices had similar AUROC in adjusted models. After a median follow-up of 6.5 years, 116 of 345 men (33.6%) had died. In adjusted models, all 4 comorbidity indices performed similarly in predicting overall survival. CONCLUSIONS: Although comorbidity is an important predictor of both curative treatment and overall survival in prostate cancer, the optimal comorbidity index for use in research remains unclear. Selecting the optimal comorbidity index may depend on both the specific patient population and the outcome being considered.
Authors: Thomas J Guzzo; Paul Dluzniewski; Ryan Orosco; Elizabeth A Platz; Alan W Partin; Misop Han Journal: Urology Date: 2010-09 Impact factor: 2.649
Authors: A Kutikov; M R Cooperberg; A T Paciorek; R G Uzzo; P R Carroll; S A Boorjian Journal: Prostate Cancer Prostatic Dis Date: 2012-06-19 Impact factor: 5.554
Authors: Michael Froehner; Anna-Elisa Kellner; Rainer Koch; Gustavo B Baretton; Oliver W Hakenberg; Manfred P Wirth Journal: BMC Urol Date: 2014-03-29 Impact factor: 2.264