Abstract
AIMS: To identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type. METHODS: We analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A(1c) (HbA(1c)) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory. RESULTS: The study population was comprised of 119,952 adults with newly diagnosed diabetes, including 696 (0.58%) with type 1 diabetes. Among patients with type 1 diabetes, 52.6% were diagnosed at very high HbA(1c), partially improved, but never achieved control; 32.5% were diagnosed at low HbA1c and deteriorated over time; and 14.9% had stable low HbA(1c). Among patients with type 2 diabetes, 67.7% had stable low HbA(1c), 14.4% were diagnosed at very high HbA(1c), partially improved, but never achieved control; 10.0% were diagnosed at moderately high HbA(1c) and deteriorated over time; and 4.9% were diagnosed at moderately high HbA(1c) and improved over time. CONCLUSIONS: Claims data identified distinct longitudinal trajectories of HbA(1c) after diabetes diagnosis, which can be used to anticipate challenges and individualize care plans to improve glycemic control.