Association of Diabetes Duration and Glycemic Control With Stroke Rate in Patients With Atrial Fibrillation and Diabetes: A Population-Based Cohort Study.
Husam Abdel-QadirMadison GunnIliana C LegaAndrea PangPeter C AustinSheldon M SinghCynthia A JackeviciusKaren TuPaul DorianDouglas S LeeDennis T KoPublished in: Journal of the American Heart Association (2022)
Background There are limited data on the association of diabetes duration and glycemic control with stroke risk in atrial fibrillation (AF). Our objective was to study the association of diabetes duration and glycated hemoglobin (HbA1c) with the rate of stroke in people with diabetes and newly diagnosed AF. Methods and Results This was a population-based cohort study using linked administrative data sets. We studied 37 209 individuals aged ≥66 years diagnosed with AF in Ontario between April 2009 and March 2019, who had diabetes diagnosed 1 to 16 years beforehand. The primary outcome was hospitalization for stroke at 1 year. Cause-specific hazard regression was used to model the association of diabetes duration and glycated hemoglobin (HbA1c) with the rate of stroke. Restricted cubic spline analyses showed increasing hazard ratios (HR) for stroke with longer diabetes duration that plateaued after 10 years and increasing HRs for stroke with HbA1c levels >7%. Relative to patients with <5 years diabetes duration, stroke rates were significantly higher for patients with ≥10 years duration (HR, 1.45; 95% CI, 1.16-1.82; P =0.001), while diabetes duration 5 to <10 years was not significantly different. Relative to glycated hemoglobin 6% to <7%, values ≥8% were associated with higher stroke rates (HR, 1.44; 95% CI, 1.12-1.84; P =0.004), while other HbA1c categories were not significantly different. Conclusions Longer diabetes duration and higher glycated hemoglobin were associated with significantly higher stroke rates in patients with AF and diabetes. Models for stroke risk prediction and preventive care in AF may be improved by considering patients' diabetes characteristics.
Keyphrases
- atrial fibrillation
- glycemic control
- type diabetes
- cardiovascular disease
- blood glucose
- catheter ablation
- left atrial
- weight loss
- newly diagnosed
- direct oral anticoagulants
- chronic kidney disease
- end stage renal disease
- insulin resistance
- healthcare
- cerebral ischemia
- artificial intelligence
- palliative care
- big data
- machine learning
- prognostic factors
- pain management
- mitral valve
- subarachnoid hemorrhage
- deep learning
- brain injury
- skeletal muscle
- peritoneal dialysis
- patient reported outcomes