Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes

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.

Keywords: Artificial intelligence; Diabetes; Glycemic control; Glycosylated hemoglobin; Hemoglobin A1c; Longitudinal trajectories; Machine learning; Personalized medicine; Precision medicine; Risk stratification; Type 1 diabetes; Type 2 diabetes.