TY - JOUR
T1 - Trajectories of Glycemic Change in a National Cohort of Adults with Previously Controlled Type 2 Diabetes
AU - McCoy, Rozalina G.
AU - Ngufor, Che
AU - Van Houten, Holly K.
AU - Caffo, Brian
AU - Shah, Nilay D.
N1 - Funding Information:
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.
Publisher Copyright:
© 2017 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - diabetes mellitus type 2
KW - glycosylated hemoglobin
KW - machine learning
KW - mixture of generalized linear mixed effects model (MGLMM)
KW - patient-centered medicine
KW - random forest feature contribution (rfFC) method
UR - http://www.scopus.com/inward/record.url?scp=85031817336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031817336&partnerID=8YFLogxK
U2 - 10.1097/MLR.0000000000000807
DO - 10.1097/MLR.0000000000000807
M3 - Article
C2 - 28922296
AN - SCOPUS:85031817336
SN - 0025-7079
VL - 55
SP - 956
EP - 964
JO - Medical care
JF - Medical care
IS - 11
ER -