Sridharan Raghavan1,2,3,4, Wenhui G Liu5, Seth A Berkowitz6, Anna E Barón5,7, Mary E Plomondon5, Thomas M Maddox8, Jane E B Reusch5,9, P Michael Ho5,10, Liron Caplan5,11. 1. Department of Veterans Affairs, Eastern Colorado Healthcare System, Aurora, CO, USA. sridharan.raghavan@ucdenver.edu. 2. Division of Hospital Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA. sridharan.raghavan@ucdenver.edu. 3. Colorado Cardiovascular Outcomes Research Consortium, Aurora, CO, USA. sridharan.raghavan@ucdenver.edu. 4. Rocky Mountain Regional VA Medical Center Medicine Service (111), 1700 North Wheeling Street, Aurora, CO, 80045, USA. sridharan.raghavan@ucdenver.edu. 5. Department of Veterans Affairs, Eastern Colorado Healthcare System, Aurora, CO, USA. 6. Division of General Medicine & Clinical Epidemiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA. 7. Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA. 8. Division of Cardiology, Washington University School of Medicine, St. Louis, MO, USA. 9. Division of Endocrinology, Metabolism, and Diabetes, University of Colorado School of Medicine, Aurora, CO, USA. 10. Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, USA. 11. Division of Rheumatology, University of Colorado School of Medicine, Aurora, CO, USA.
Abstract
BACKGROUND: The relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD). OBJECTIVE: To compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD DESIGN: Retrospective observational cohort study PARTICIPANTS: Diabetes patients (n = 7780) with and without angiographically defined CAD MAIN MEASURES: We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates. KEY RESULTS: Three HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7-9% and ≥ 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1). CONCLUSIONS: In clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records.
BACKGROUND: The relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD). OBJECTIVE: To compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD DESIGN: Retrospective observational cohort study PARTICIPANTS: Diabetes patients (n = 7780) with and without angiographically defined CAD MAIN MEASURES: We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates. KEY RESULTS: Three HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7-9% and ≥ 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1). CONCLUSIONS: In clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records.
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