Rozalina G McCoy1, Che Ngufor, Holly K Van Houten, Brian Caffo, Nilay D Shah. 1. 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.
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 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.
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.
Authors: Seth A Berkowitz; James B Meigs; Darren DeWalt; Hilary K Seligman; Lily S Barnard; Oliver-John M Bright; Marie Schow; Steven J Atlas; Deborah J Wexler Journal: JAMA Intern Med Date: 2015-02 Impact factor: 21.873
Authors: Paul J Wallace; Nilay D Shah; Taylor Dennen; Paul A Bleicher; Paul D Bleicher; William H Crown Journal: Health Aff (Millwood) Date: 2014-07 Impact factor: 6.301
Authors: Jennifer M Rohan; Joseph R Rausch; Jennifer Shroff Pendley; Alan M Delamater; Lawrence Dolan; Grafton Reeves; Dennis Drotar Journal: Health Psychol Date: 2013-11-25 Impact factor: 4.267
Authors: Mark A Clements; Marcus Lind; Sripriya Raman; Susana R Patton; Kasia J Lipska; Amanda G Fridlington; Fengming Tang; Phil G Jones; Yue Wu; John A Spertus; Mikhail Kosiborod Journal: BMJ Open Diabetes Res Care Date: 2014-10-07
Authors: Konstantinos Poulakis; Joana B Pereira; J-Sebastian Muehlboeck; Lars-Olof Wahlund; Örjan Smedby; Giovanni Volpe; Colin L Masters; David Ames; Yoshiki Niimi; Takeshi Iwatsubo; Daniel Ferreira; Eric Westman Journal: Nat Commun Date: 2022-08-05 Impact factor: 17.694