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

Dados Bibliográficos

ID9104425
AutoresRozalina G Mccoy (0000-0002-2289-3183, Medicine, Division of Primary Care Internal Medicine, autor correspondente), Che Ngufor (0000-0001-5935-5744, Department of Health Sciences Research, Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN), Holly K Van Houten (0000-0003-4981-4868, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery), Brian Caffo (0000-0002-0793-9497, Johns Hopkins University), Nilay D Shah (0000-0002-8597-3447, Health Sciences Research, Division of Health Care Policy & Research, Mayo Clinic)
Ano2017
Volume55
Fascículo11
Páginas956-964
Data de publicação2017-11-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000807
PMID28922296
OpenAlexW2756376910
IdiomaEN
Citações recebidas1
Referências citadas24

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

Cohort · Diabetes mellitus · Glycated hemoglobin · Glycemic · Hemoglobin · Hemoglobin A · Type 2 diabetes · Diabetes Management and Research · Diabetes Treatment and Management · Diabetes, Cardiovascular Risks, and Lipoproteins · Endocrinology · Internal Medicine · Medicine