Abstract
OBJECTIVE: To investigate the two-dimensional speckle-tracking echocardiography (2D-STE) parameters associated with early impaired left ventricular systolic function in SLE patients and to estimate the potential clinical factors that may trigger and influence left ventricular systolic dysfunction. METHODS: This study collected a total of 36 patients admitted to the rheumatology and immunology department of Sun Yat-sen University between January 2020 and December 2021, who were newly diagnosed with SLE and had a Systemic Lupus Erythematosus Disease Activity Index 2000 Score≥4 points. An equal number of healthy controls matched for gender and age were included. All participants underwent routine echocardiography and two-dimensional speckle-tracking echocardiography (2D-STE) examinations. Various clinical data were also collected. Machine learning and regressions were used to estimate potential risk factors for left ventricular systolic dysfunction in SLE patients. RESULTS: Significant differences in 2D-STE parameters were found, including global longitudinal peak systolic strain (GLPS) (p-adjust<0.001), GLPS strain obtained from the apical two-chamber view and GLPS strain obtained from the apical four-chamber view (GLPS-A4C) (p-adjust=0.005), and GLPS strain obtained from the apical long-axis view (GLPS-APLAX) (p-adjust=0.003) between SLE patients and controls. Machine learning models, particularly GLPS-APLAX, showed excellent discrimination ability with an AUC of 0.93 (95% CI: 0.89 to 0.96) and an area under the precision-recall curve of 0.96. Multivariate regression further highlighted the inverse relationship between anti-U1 small nuclear ribonucleoprotein (U1RNP) antibodies and four GLPS-related continuous variable measures, with GLPS, GLPS-A4C and GLPS-APLAX measures having statistically significant effects (eg, GLPS coefficient=-3.71, 95% CI: -5.91 to -1.51, p=0.002). CONCLUSIONS: This case-control study revealed that 2D-STE parameters can be used to predict subclinical cardiac dysfunction in SLE patients, and anti-U1RNP antibodies may be an essential predictive clinical factor. Machine learning may further assist in preliminary screening and quantifying left ventricular systolic dysfunction reasons in SLE patients.