Enhancing predictions of subclinical cardiac dysfunction in SLE patients through integrative machine learning analysis

Q: What was the primary aim of the study?

To identify 2D-STE parameters linked to early impaired left ventricular systolic function in SLE and to estimate clinical factors that may influence that dysfunction.

Q: How were risk factors evaluated?

The study used machine learning models and regression analyses based on echocardiography (including 2D-STE) and collected clinical data.

Q: Which finding relates SLE antibodies to cardiac function?

Multivariate regression highlighted an inverse relationship between anti-U1 small nuclear ribonucleoprotein (U1RNP) antibodies and multiple GLPS-related measures, indicating anti-U1RNP as an important predictive factor.

Aug 4, 2026

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This case-control study investigates which two-dimensional speckle-tracking echocardiography (2D-STE) parameters reflect early left ventricular systolic impairment in systemic lupus erythematosus (SLE) and which clinical factors may drive or modulate dysfunction. Data were collected from 36 newly diagnosed SLE patients (SLEDAI-2000≥4) and matched healthy controls. Routine echocardiography and 2D-STE were performed, and machine learning with regression models estimated SLE-specific risk factors. SLE patients showed significant reductions in multiple global longitudinal strain measures, and models—especially GLPS-APLAX—demonstrated strong discrimination.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Objectives and methods

Key results

Conclusions

What is already known on this topic

What this study adds

How this study might affect research, practice or policy

Introduction

Co-morbidities To cite: Liu Y, Xie S, Lin Z, et al. Enhancing predictions of subclinical cardiac dysfunction in SLE patients through integrative machine learning analysis. Lupus Science & Medicine 2025;12:e001616 . doi:10 . 1136/ lupus-2025-001616 ► Additional supplemental material is published online only. To view, please visit the journal online ([https://doi.org/10.1136/](https://doi.org/10.1136/)[ ](https://doi.org/10.1136/)[lupus-2025-001616](lupus-2025-001616)) . YL and SX contributed equally. Received 3 April 2025 Accepted 13 August 2025 © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group. 1Department of Rheumatology and Immunology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China 2Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA 3Johns Hopkins University School of Medicine, Baltimore, Maryland, USA 4Department of Rheumatology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China 5Department of Cardiovascular, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China Correspondence to Dr Zhiming Lin; lzm-zj99@163. com Enhancing predictions of subclinical cardiac dysfunction in SLE patients through integrative machine learning analysis Yuhong Liu ,1 Siwei Xie ,2,3 Zhiming Lin,4 Changlin Zhao5 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 speckletracking 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 (GLPSA4C) (p-adjust=0 . 005), and GLPS strain obtained from the apical long-axis view (GLPS-APLAX) (padjust=0 . 003) between SLE patients and controls. Machine learning models, particularly GLPS-APLAX, showed excellent discrimination ability with an AUCof 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. WHAT IS ALREADY KNOWN ON THIS TOPIC | ⇒ SLE patients are at high risk for cardiovascular diseases, two-dimensional speckle-tracking echocardiography (2D-STE) and global longitudinal strain are more sensitive for detecting subclinical heart dysfunction in SLE patients.
⇒ Anti-U1 small nuclear ribonucleoprotein (U1RNP) antibodies are linked to so

FAQs

What was the primary aim of the study?

To identify 2D-STE parameters linked to early impaired left ventricular systolic function in SLE and to estimate clinical factors that may influence that dysfunction.

How were risk factors evaluated?

The study used machine learning models and regression analyses based on echocardiography (including 2D-STE) and collected clinical data.

Which finding relates SLE antibodies to cardiac function?

Multivariate regression highlighted an inverse relationship between anti-U1 small nuclear ribonucleoprotein (U1RNP) antibodies and multiple GLPS-related measures, indicating anti-U1RNP as an important predictive factor.

Aug 4, 2026

Upload by Evangeline