Abstract
Studies on outcomes in bladder cancer rely on accurate methods to identify patients with bladder cancer and differentiate bladder cancer stage. Medical record and administrative databases are increasingly used to study cancer incidence, but few have distinguished cancer stage, and none have focused on bladder cancer. In this study, we used data from The UK Health Improvement Network (THIN) to identify patients with bladder cancer using at least one diagnostic code for bladder cancer, and distinguish muscle-invasive from non-invasive disease using a subsequent code for cystectomy. Algorithms were validated against a gold standard of physician-completed questionnaires, pathology reports, and consultant letters. Algorithm performance was evaluated by measuring positive predictive value (PPV) and corresponding 95% confidence interval (CI). Among all patients coded with bladder cancer (n = 194), PPV for any bladder cancer was 99.5% (95% CI, 97.2-99.9). PPV for incident bladder cancer was 93.8% (95% CI, 89.4-96.7). PPV for muscle-invasive bladder cancer was 70.1% (95% CI, 59.4-79.5) in patients with cystectomy (n = 95) and 83.9% (95% CI, 66.3-94.5) in those with cystectomy plus additional codes for metastases and death (n = 31). Using our codes for bladder cancer, the age- and sex-standardized incidence rate (SIR) of bladder cancer in THIN approximated that measured by cancer registries (SIR within 20%), suggesting that sensitivity was high as well. THIN is a valid and novel database for the study of bladder cancer. Our algorithm can be used to examine the epidemiology of muscle-invasive bladder cancer or outcomes following cystectomy for patients with muscle invasion.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.