Type Two Diabetes Patients in Latvia: A Prototype Model for Predicting Two-Year Ahead Risk of Ischemic Heart Disease Hospitalization Using Administrative Health Records Data

Background

and Objectives: Diabetes mellitus (Type 2 diabetes) is a significant and rapidly growing global healthcare challenge and a leading cause of severe complications, including cardiovascular disease (CVD), chronic kidney disease and other conditions. In Latvia more than 103 000 individuals have been registered in the Latvian Diabetes Patient Register, of whom 90% had type 2 diabetes. Since early diagnosis can reduce complication risk, considerable effort has been devoted to the development of numerous CVD risk prediction tools using traditional statistics as well as data science-based methods. This article augments these efforts with predictive models built specifically for Latvia’s diabetes patient population. Materials and Methods: Models used anonymized diabetes patient data stored at Latvia’s Center for Disease Prevention and Control containing routinely collected information on patients’ utilization of publicly funded healthcare services. No clinical data were included. In keeping with best practices in model development, 96 302 available patient records were randomly partitioned into a 70% training data portion and 30% for out-of-sample validation. We tested three modeling methodologies – logistic regression, Random Forests, and XGBoost.