Abstract P093: Influence of Comorbidities on Outcomes in Atrial Fibrillation: A Population-based Case-control Study

Abstract

Background: Differences in the impact of comorbid conditions on outcomes in atrial fibrillation (AF) patients compared to population controls have not been well documented. Methods: The prevalence of 19 chronic conditions and smoking status was obtained in 1430 patients with incident AF from 2000-2010 and 1430 controls matched 1:1 on sex and age (within 5 years) from Olmsted County, MN. Andersen-Gill models determined associations of each condition with all-cause hospitalizations in AF cases and controls after adjusting for all other conditions and accounting for the matching. Cox regression determined associations of each condition with death. Results: Among 1430 matched pairs (median age 76 years, 48.6% men), the prevalence of chronic conditions was higher in AF cases compared to controls for all conditions except asthma, dementia, depression, hepatitis, and osteoporosis. Over a mean follow-up of 6.3 years, 2678 hospitalizations and 812 deaths occurred. The rates of hospitalization were 59 and 26 per 100 person-years and the rates of death were 10 and 5 per 100 person-years in AF cases and controls, respectively. After adjusting for all other conditions, the risk of hospitalization was lower in AF patients compared to controls for those with coronary artery disease, arthritis, cancer, chronic obstructive pulmonary disease, and osteoporosis (figure). In contrast, the risk of hospitalization was higher in AF cases for those with diabetes and substance abuse. For deaths, the only comorbidity with different associations between AF cases and controls was depression. The hazard ratios (95% CI) for death were 2.02 (1.26-3.24) in AF cases and 0.90 (0.58-1.38) in controls (p-value for interaction=0.008). Conclusions: AF patients have a higher prevalence of chronic conditions compared to population controls. The associations of comorbidities with hospitalizations differed between AF cases and controls, suggesting that management of comorbidities in patients with AF may need to be tailored to this specific patient population.

Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information

“…Atrial Fibrillation (AF) is the most common arrhythmia encountered in clinical practice, affecting over 30 million patients worldwide (1,2). Beyond the age of 40, ∼26% of men and 23% of women will develop AF (3,4), a diagnosis associated with elevated risk of cardioembolic stroke (5), reduced quality of life (4), and higher risk of heart failure (HF) related events (6)(7)(8)(9). Targeted efforts to develop and validate AF risk scores have been described (6,8,(10)(11)(12) leveraging data from healthy populations without cardiovascular disease.…”