Validation of algorithms to identify colorectal cancer patients from administrative claims data of a Japanese hospital

BACKGROUND: Administrative claims data are a valuable source for clinical studies; however, the use of validated algorithms to identify patients is essential to minimize bias. We evaluated the validity of diagnostic coding algorithms for identifying patients with colorectal cancer from a hospital’s administrative claims data. METHODS: This validation study used administrative claims data from a Japanese university hospital between April 2017 and March 2019. We developed diagnostic coding algorithms, basically based on the International Classification of Disease (ICD) 10th codes of C18–20 and Japanese disease codes, to identify patients with colorectal cancer. For random samples of patients identified using our algorithms, case ascertainment was performed using chart review as the gold standard. The positive predictive value (PPV) was calculated to evaluate the accuracy of the algorithms. RESULTS: Of 249 random samples of patients identified as having colorectal cancer by our coding algorithms, 215 were confirmed cases, yielding a PPV of 86.3% (95% confidence interval [CI], 81.5–90.1%). When the diagnostic codes were restricted to site-specific (right colon, left colon, transverse colon, or rectum) cancer codes, 94 of the 100 random samples were true cases of colorectal cancer. Consequently, the PPV increased to 94.0% (95% CI, 87.2–97.4%). CONCLUSION: Our diagnostic coding algorithms based on ICD-10 codes and Japanese disease codes were highly accurate in detecting patients with colorectal cancer from this hospital’s claims data. The exclusive use of site-specific cancer codes further improved the PPV from 86.3 to 94.0%, suggesting their desirability in identifying these patients more precisely. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09266-1.