Electrocardiogram (ECG) abnormalities are predictive of subsequent cardiovascular events. Cardiac involvement is common in systemic lupus erythematosus (SLE). We aimed to determine the prevalence of ECG abnormalities in SLE patients and to examine the factors associated with ECG abnormalities with machine learning approaches.
Consecutive SLE patients' records were retrieved from the database of the hospital for the cross-sectional study. Abnormal ECGs with clinical significance were grouped into tachyarrhythmias, atrioventricular block, non-specific ST-segment changes, T-wave abnormalities, ventricular hypertrophy, axis deviation, bundle branch block, and QT interval prolongation. Associated factors of the most common ECG abnormalities were assessed by comparing logistic regression and four other machine learning approaches.
Two hundred ninety-nine patients were enrolled. One hundred twenty-eight out of them were clinically significant abnormalities. T-wave changes (52.3%), non-specific ST-T changes (26.6%), and prolonged QT interval (8.6%) were the most prevalent abnormalities among patients with abnormal ECG. Random forest models had the best performance in the discovery of associated factors. Age, disease duration, ANA titer, disease activity (SLEDAI-2K) were associated with non-specific ST-T changes, prolonged QT interval, and T-wave changes. Hypertension, positive anti-SSA, and secondary Sjögren syndrome were influential factors for non-specific ST-T changes, prolonged QT interval, and T-wave changes specifically.
ST-T changes/T-wave changes were the most common abnormalities seen in ECGs of SLE patients. Our finding suggests that age, longer disease duration, higher disease activity, hypertension, anti-SSA antibody positive, and secondary Sjögren syndrome are important, influential factors for these ECG abnormalities.