Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital

BACKGROUND: Validated coding algorithms are essential to generate high-quality, real-world evidence from claims data studies. OBJECTIVE: We aimed to evaluate the validity of the algorithms to identify patients with bone metastases using claims data from a Japanese hospital. PATIENTS AND METHODS: This study used administrative claims data and electronic medical records at Juntendo University Hospital from April 2017 to March 2019. We developed two candidate claims-based algorithms to detect bone metastases, one based on diagnosis codes alone (Algorithm 1) and the other based on the combination of diagnosis and imaging test codes (Algorithm 2). Of the patients identified by Algorithm 1, 100 patients were randomly sampled. Among these 100 patients, 88 patients met the conditions of Algorithm 2; further, 12 additional patients were randomly sampled from those identified by Algorithm 2, thus obtaining a total of 100 patients for Algorithm 2. They were evaluated for their true diagnosis using the patient chart review as the gold standard. The positive predictive value (PPV) was calculated to assess the accuracy of each algorithm. RESULTS: For Algorithm 1, 82 patients were analyzed after excluding 18 patients without diagnostic imaging reports. Of these, 69 patients were true positive by chart review, resulting in a PPV of 84.1% (95% confidence interval (CI) 74.5–90.6). For Algorithm 2, 92 patients were analyzed after excluding eight patients whose diagnoses were not judged by chart review. Of these, 76 patients were confirmed positive by chart review, yielding a PPV of 82.6% (95% CI 73.4–89.1). CONCLUSION: Both claims-based algorithms yielded high PPVs of approximately 85%, with no improvement in PPV by adding imaging test conditions. The diagnosis code-based algorithm is sufficient and valid for identifying bone metastases in this Japanese hospital. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40801-022-00347-x.