Validation of novel identification algorithms for major adverse cardiovascular events in a Japanese claims database

Predicting clinical outcomes can be difficult, particularly for life-threatening events with a low incidence that require numerous clinical cases. Our aim was to develop and validate novel algorithms to identify major adverse cardiovascular events (MACEs) from claims databases. We developed algorithms based on the data available in the claims database International Classification of Diseases, Tenth Revision (ICD-10), drug prescriptions, and medical procedures. We also employed data from the claims database of Jichi Medical University Hospital, Japan, for the period between October 2012 and September 2014. In total, we randomly extracted 100 potential acute myocardial infarction cases and 200 potential stroke cases (ischemic and hemorrhagic stroke were analyzed separately) based on ICD-10 diagnosis. An independent committee reviewed the corresponding clinical data to provide definitive diagnoses for the extracted cases. We then assessed the algorithms' accuracy using positive predictive values (PPVs) and apparent sensitivities. The PPVs of acute myocardial infarction, ischemic stroke, and hemorrhagic stroke were low only by diagnosis (81.6% [95% CI 72.5-88.7]; 31.0% [95% CI 22.8-40.3]; and 45.5% [95% CI 34.1-57.2], respectively); however, the PPVs were elevated after adding the prescription and procedure data (87.0% [95% CI 78.3-93.1]; 44.4% [95% CI 32.7-56.6]; and 46.1% [95% CI 34.5-57.9], respectively). When we added event-specific prescription and procedure data to the algorithms, the PPVs for each event increased to 70%-98%, with apparent sensitivities exceeding 50%. Algorithms that rely on ICD-10 diagnosis in combination with data on specific drugs and medical procedures appear to be valid for identifying MACEs in Japanese claims databases.