Prevalence And Risk Factors of Electrocardiogram Abnormalities In Patients With Rheumatoid Arthritis: A Machine Learning Study With Follow-Up Data

OBJECTIVES: Electrocardiogram (ECG) abnormalities could predict some subsequent cardiovascular events. Cardiac involvement is a major extra-articular manifestation in rheumatoid arthritis (RA). We aimed to determine the prevalence of three major ECG abnormalities in RA patients, discover the associated ECG abnormalities associated with machine learning (ML) approaches, and then examine these preselected factors in the follow-up patients with traditional Cox regression. METHODS: Consecutive RA patients’ records were retrieved from the hospital database; about one-third of patients had follow-up data. Abnormal ECGs with clinical significance were grouped into non-specific ST-segment/T-wave changes, QT interval prolongation, and QRS-T angle increase. Machine learning approaches assessed the associated factors of these abnormalities. The top-important factors selected by the most optimal ML would be used to construct Cox regression models. RESULTS: Two hundred twenty-six patients were enrolled for the first step cross-sectional study. Non-specific ST-T changes (27%) were the most prevalent abnormalities among patients with abnormal ECGs. Random forest models had the best performance in the discovery of associated factors for three outcomes. Cox regression validated that rheumatoid factor and low-density lipoprotein were common risk factors within those three abnormalities. Hypertension, ESR, and serum immunoglobulin G were influential factors for non-specific ST-T changes, prolonged QT interval, and increased QRS-T angle specifically. CONCLUSION: Non-specific ST-T changes were the most common abnormalities seen in ECGs of RA patients. Our finding suggests that rheumatoid factor, LDL, hypertension, and inflammatory indicators are important risk factors for these ECG abnormalities.