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

Rozalina G. McCoy, Che Ngufor, Holly K. Van Houten, Brian Caffo, Nilay D. Shah

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background: Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control. Objectives: To identify patterns of hemoglobin A 1c (HbA 1c) 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 HbA 1c <7.0%. Measures: HbA 1c 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 HbA 1c. The k=3 model had the best fit, reflecting 3 distinct trajectories of glycemic change: (T1) rapidly deteriorating HbA 1c among 302 (1.1%) youngest (mean, 55.2 y) patients with lowest mean baseline HbA 1c, 6.05%; (T2) gradually deteriorating HbA 1c among 902 (3.3%) patients (mean, 56.5 y) with highest mean baseline HbA 1c, 6.53%; and (T3) stable glycemic control among 25,800 (95.5%) oldest (mean, 58.5 y) patients with mean baseline HbA 1c 6.21%. After 24 months, HbA 1c 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.

Original languageEnglish (US)
Pages (from-to)956-964
Number of pages9
JournalMedical care
Volume55
Issue number11
DOIs
StatePublished - 2017

Keywords

  • diabetes mellitus type 2
  • glycosylated hemoglobin
  • machine learning
  • mixture of generalized linear mixed effects model (MGLMM)
  • patient-centered medicine
  • random forest feature contribution (rfFC) method

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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