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Trajectories of Glycemic Change in a National Cohort of Adults With Previously Controlled Type 2 Diabetes

McCoy, Rozalina G. MD, MS*,†,‡; Ngufor, Che PhD§; Van Houten, Holly K. BA; Caffo, Brian PhD; Shah, Nilay D. PhD†,¶

doi: 10.1097/MLR.0000000000000807
Original Articles

Background: Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control.

Objectives: To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes.

Research Design: Cohort study using OptumLabs Data Warehouse, 2001–2013. We develop and apply a machine learning framework that uses a Bayesian estimation of the mixture of generalized linear mixed effect models to discover glycemic trajectories, and a random forest feature contribution method to identify patient characteristics predictive of their future glycemic trajectories.

Subjects: The study cohort consisted of 27,005 US adults with type 2 diabetes, age 18 years and older, and stable index HbA1c <7.0%.

Measures: HbA1c values during 24 months of observation.

Results: We compared models with k=1, 2, 3, 4, 5 trajectories and baseline variables including patient age, sex, race/ethnicity, comorbidities, medications, and HbA1c. The k=3 model had the best fit, reflecting 3 distinct trajectories of glycemic change: (T1) rapidly deteriorating HbA1c among 302 (1.1%) youngest (mean, 55.2 y) patients with lowest mean baseline HbA1c, 6.05%; (T2) gradually deteriorating HbA1c among 902 (3.3%) patients (mean, 56.5 y) with highest mean baseline HbA1c, 6.53%; and (T3) stable glycemic control among 25,800 (95.5%) oldest (mean, 58.5 y) patients with mean baseline HbA1c 6.21%. After 24 months, HbA1c rose to 8.75% in T1 and 8.40% in T2, but remained stable at 6.56% in T3.

Conclusions: Patients with controlled type 2 diabetes follow 3 distinct trajectories of glycemic control. This novel application of advanced analytic methods can facilitate individualized and population diabetes care by proactively identifying high risk patients.

Supplemental Digital Content is available in the text.

Departments of *Medicine, Division of Primary Care Internal Medicine

Health Sciences Research, Division of Health Care Policy & Research, Mayo Clinic

Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery

§Department of Health Sciences Research, Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN

Department of Biostatistics, Johns Hopkins University, Baltimore, MD

OptumLabs, Cambridge, MA

Parts of this work were presented as a poster presentation at the American Diabetes Association 76th Scientific Sessions in New Orleans, LA in June 2016 and as an oral presentation at the Academy Health Research Meeting in Boston, MA in June 2016.

R.G.M. is supported by the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery and by the National Institute Of Diabetes And Digestive And Kidney Diseases of the National Institutes of Health under Award Number K23DK114497. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The remaining authors declare no conflict of interest.

Reprints: Rozalina G. McCoy, MD, Department of Medicine, Division of Primary Care Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. E-mail:

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