Prediction of Cardiovascular Complications in Diabetes from Pharmacy Administrative Claims

Abstract

Diabetes is a chronic disease characterised by high blood glucose levels, resulting in reduced life expectancy and requiring lifelong treatment. Because of their frequent necessity to seek healthcare services, diabetic patients generate huge volumes of administrative claims, detailing their healthcare encounters and prescription history. In the Veneto region (NE Italy), as in the rest of the country, these data are collected systematically and cover all eligible beneficiaries (~5 million in Veneto). Since cardiovascular complications are the main drivers of excess mortality in diabetes, forecasting their onset is desirable for patients’ care and policymaking. Hence, in the present work, we investigate the possibility of predicting 3-year cardiovascular outcomes following a 1-year baseline period of pharmacy data collection. We implement an approach based on recurrent neural networks that combines the chronologically-ordered sequence of prescriptions filled by a diabetic patient and basic biographical information (age, gender, estimated diabetes duration) to determine whether he or she will experience a 4P-MACE (4-point major adverse cardiovascular event; defined as death, myocardial infarction, stroke, or heart failure) endpoint. We develop our model with the data of 97,466 known diabetic patients identified using a validated claims-based algorithm. Independent performance tests on 4,873 subjects yield an area under the receiver-operating characteristic curve of 0.791 and a concordance index of 0.765 for the 4P-MACE primary outcome. We find death to be the easiest 4P-MACE component to predict (AUROC = 0.846), followed by heart failure (0.796), stroke (0.714), and, finally, myocardial infarction (0.708). Secondary, stratified experiments highlight independence of performance from gender, age, and number of prescriptions filled in a year with respect to the primary outcome. To the best of our knowledge, this is the first large-scale prediction model of cardiovascular complications in medication-treated diabetes solely based on sequences of filled prescriptions collected from administrative claims.