Dataset Information

Burden of atrial fibrillation in patients with rheumatic diseases.

ABSTRACT:

Background

Studies have suggested that atrial fibrillation (AF) in patients with rheumatic diseases (RD) may be due to inflammation.

Aim

To determine the highest association of AF among hospitalized RD patients and to determine morbidity and mortality associated with AF in hospitalized patients with RD.

Methods

The National inpatient sample database from October 2015 to December 2017 was analyzed to identify hospitalized patients with RD with and without AF. A subgroup analysis was performed comparing outcomes of AF among different RD.

Results

The prevalence of AF was 23.9% among all patients with RD (n = 3949203). Among the RD subgroup, the prevalence of AF was highest in polymyalgia rheumatica (33.2%), gout (30.2%), and pseudogout (27.1%). After adjusting for comorbidities, the odds of having AF were increased with gout (1.25), vasculitis (1.19), polymyalgia rheumatica (1.15), dermatopolymyositis (1.14), psoriatic arthropathy (1.12), lupus (1.09), rheumatoid arthritis (1.05) and pseudogout (1.04). In contrast, enteropathic arthropathy (0.44), scleroderma (0.96), ankylosing spondylitis (0.96), and Sjorgen's syndrome (0.94) had a decreased association of AF. The mortality, length of stay, and hospitalization costs were higher in patients with RD having AF vs without AF. Among the RD subgroup, the highest mortality was found with scleroderma (4.8%), followed by vasculitis (4%) and dermatopolymyositis (3.5%).

Conclusion

A highest association of AF was found with gout followed by vasculitis, and polymyalgia rheumatica when compared to other RD. Mortality was two-fold higher in patients with RD with AF.

SUBMITTER: Khan MZ

PROVIDER: S-EPMC8107898 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Atrial fibrillation (AF) has heterogeneous patterns of presentation concerning symptoms, duration of episodes, AF burden, and the tendency to progress towards the terminal step of permanent AF. AF is associated with a risk of stroke/thromboembolism traditionally considered dependent on patient-level risk factors rather than AF type, AF burden, or other characterizations. However, the time spent in AF appears related to an incremental risk of stroke, as suggested by the higher risk of stroke in patients with clinical AF vs. subclinical episodes and in patients with non-paroxysmal AF vs. paroxysmal AF. In patients with device-detected atrial tachyarrhythmias, AF burden is a dynamic process with potential transitions from a lower to a higher maximum daily arrhythmia burden, thus justifying monitoring its temporal evolution. In clinical terms, the appearance of the first episode of AF, the characterization of the arrhythmia in a specific AF type, the progression of AF, and the response to rhythm control therapies, as well as the clinical outcomes, are all conditioned by underlying heart disease, risk factors, and comorbidities. Improved understanding is needed on how to monitor and modulate the effect of factors that condition AF susceptibility and modulate AF-associated outcomes. The increasing use of wearables and apps in practice and clinical research may be useful to predict and quantify AF burden and assess AF susceptibility at the individual patient level. This may help us reveal why AF stops and starts again, or why AF episodes, or burden, cluster. Additionally, whether the distribution of burden is associated with variations in the propensity to thrombosis or other clinical adverse events. Combining the improved methods for data analysis, clinical and translational science could be the basis for the early identification of the subset of patients at risk of progressing to a longer duration/higher burden of AF and the associated adverse outcomes.