Madhur Nayan1, Robert J Hamilton1, Antonio Finelli1, Peter C Austin2,3, Girish S Kulkarni1, David N Juurlink4. 1. Division of Urology, Departments of Surgery and Surgical Oncology, Princess Margaret Cancer Centre, University Health Network and the University of Toronto, Toronto, ON, Canada. 2. Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada. 3. Institute of Health Management, Policy and Evaluation, University of Toronto; Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, ON, Canada. 4. Department of Internal Medicine, Sunnybrook Health Sciences Centre, University of Toronto; Toronto, ON, Canada.
Abstract
INTRODUCTION: Variables, such as smoking and obesity, are rarely available in administrative databases. We explored the added value of including these data in an administrative database study evaluating the association of statin use with survival in kidney cancer. METHODS: We linked administrative data with chart-abstracted data on smoking and obesity for 808 patients undergoing nephrectomy for kidney cancer. Base models consisted of variables from administrative databases (age, sex, year of surgery, and different measures of comorbidity [to compare their sensitivity to smoking and obesity data]); extended models added chart-abstracted data. We compared coefficients for statin use with overall (OS) and cancer-specific survival (CSS), and used the c-statistic and net reclassification improvement (NRI) to compare predications of five-year survival obtained from Cox proportional hazard models. RESULTS: The coefficient for statin use changed minimally following addition of abstracted data (<6% for OS, <2% for CSS). Base models performed similarly for OS, with c-statistics of 0.75 (95% confidence interval [CI] 0.72-0.79) for Charlson score and 0.73 (95% CI 0.69-0.78) for John Hopkins Aggregated Diagnosis Groups score. After including abstracted data, c-statistics modestly improved (change <0.02); CSS demonstrated similar findings. NRIs were 0.210 (95% CI 0.062-0.297) and 0.186 (-0.031-0.387) when using the Charlson score, and 0.207 (0.068-0.287) and 0.197 (0.007-0.399) when using the Aggregated Diagnosis Groups score, for OS and CSS, respectively. CONCLUSIONS: The inclusion of data on smoking and obesity marginally influences survival models in kidney cancer studies using administrative data.
INTRODUCTION: Variables, such as smoking and obesity, are rarely available in administrative databases. We explored the added value of including these data in an administrative database study evaluating the association of statin use with survival in kidney cancer. METHODS: We linked administrative data with chart-abstracted data on smoking and obesity for 808 patients undergoing nephrectomy for kidney cancer. Base models consisted of variables from administrative databases (age, sex, year of surgery, and different measures of comorbidity [to compare their sensitivity to smoking and obesity data]); extended models added chart-abstracted data. We compared coefficients for statin use with overall (OS) and cancer-specific survival (CSS), and used the c-statistic and net reclassification improvement (NRI) to compare predications of five-year survival obtained from Cox proportional hazard models. RESULTS: The coefficient for statin use changed minimally following addition of abstracted data (<6% for OS, <2% for CSS). Base models performed similarly for OS, with c-statistics of 0.75 (95% confidence interval [CI] 0.72-0.79) for Charlson score and 0.73 (95% CI 0.69-0.78) for John Hopkins Aggregated Diagnosis Groups score. After including abstracted data, c-statistics modestly improved (change <0.02); CSS demonstrated similar findings. NRIs were 0.210 (95% CI 0.062-0.297) and 0.186 (-0.031-0.387) when using the Charlson score, and 0.207 (0.068-0.287) and 0.197 (0.007-0.399) when using the Aggregated Diagnosis Groups score, for OS and CSS, respectively. CONCLUSIONS: The inclusion of data on smoking and obesity marginally influences survival models in kidney cancer studies using administrative data.
Authors: Alexander S Parker; Christine M Lohse; John C Cheville; David D Thiel; Bradley C Leibovich; Michael L Blute Journal: Urology Date: 2006-10 Impact factor: 2.649
Authors: Ashish M Kamat; Ryan P Shock; Yoshio Naya; Charles J Rosser; Joel W Slaton; Louis L Pisters Journal: Urology Date: 2004-01 Impact factor: 2.649
Authors: Axel Haferkamp; Maria Pritsch; Jens Bedke; Nina Wagener; Jesco Pfitzenmaier; Stephan Buse; Markus Hohenfellner Journal: BJU Int Date: 2008-02-05 Impact factor: 5.588