Validation of a coding algorithm to identify bladder cancer and distinguish stage in an electronic medical records database

• Published in: Cancer epidemiology, biomarkers & prevention biomarkers & prevention, 2015-01, Vol.24 (1), p.303-307
• Main Authors: Mamtani, Ronac, Haynes, Kevin, Boursi, Ben, Scott, Frank I, Goldberg, David S, Keefe, Stephen M, Vaughn, David J, Malkowicz, S Bruce, Lewis, James D
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Cross-Sectional Studies; Electronic Health Records; Female; Humans; Male; Surveys and Questionnaires; Urinary Bladder Neoplasms - pathology
• ISSN: 1055-9965; 1538-7755
• DOI: 10.1158/1055-9965.epi-14-0677

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.