{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T01:20:03Z","timestamp":1782782403716,"version":"3.54.5"},"reference-count":45,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Key Research and Development Project","award":["2023B03J1244"],"award-info":[{"award-number":["2023B03J1244"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Lupus Sci Med"],"accepted":{"date-parts":[[2025,8,13]]},"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u22654 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Significant differences in 2D-STE parameters were found, including global longitudinal peak systolic strain (GLPS) (p-adjust&lt;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=\u22123.71, 95%\u2009CI: \u22125.91 to \u22121.51, p=0.002).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/lupus-2025-001616","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T17:20:11Z","timestamp":1756747211000},"page":"e001616","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing predictions of subclinical cardiac dysfunction in SLE patients through integrative machine learning analysis"],"prefix":"10.1136","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3030-8327","authenticated-orcid":false,"given":"Yuhong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Rheumatology and Immunology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1602-2296","authenticated-orcid":false,"given":"Siwei","family":"Xie","sequence":"additional","affiliation":[{"name":"Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"name":"Johns Hopkins University School of Medicine, Baltimore, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Rheumatology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changlin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"2026010700553358000_12.2.e001616.1","doi-asserted-by":"crossref","first-page":"107466","DOI":"10.1016\/j.intimp.2021.107466","article-title":"Patients with systemic lupus erythematosus face a high risk of cardiovascular disease: A systematic review and Meta-analysis","volume":"94","author":"Lu","year":"2021","journal-title":"Int Immunopharmacol"},{"key":"2026010700553358000_12.2.e001616.2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ejim.2019.12.033","article-title":"Speckle tracking echocardiography in patients with systemic lupus erythematosus: A meta-analysis","volume":"73","author":"Di Minno","year":"2020","journal-title":"Eur J Intern Med"},{"key":"2026010700553358000_12.2.e001616.3","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.mayocp.2018.07.017","article-title":"Echocardiographic Assessment of Left Ventricular Systolic Function: An Overview of Contemporary Techniques, Including Speckle-Tracking Echocardiography","volume":"94","author":"Luis","year":"2019","journal-title":"Mayo Clin Proc"},{"key":"2026010700553358000_12.2.e001616.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2017.11.017"},{"key":"2026010700553358000_12.2.e001616.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.echo.2010.07.019"},{"key":"2026010700553358000_12.2.e001616.6","first-page":"356","article-title":"Prediction of All-Cause Mortality From Global Longitudinal Speckle Strain","volume":"2","author":"Stanton","year":"2009","journal-title":"Circ: Cardiovascular Imaging"},{"key":"2026010700553358000_12.2.e001616.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2012.05.039"},{"key":"2026010700553358000_12.2.e001616.8","doi-asserted-by":"publisher","DOI":"10.1093\/ehjci\/jes155"},{"key":"2026010700553358000_12.2.e001616.9","doi-asserted-by":"publisher","DOI":"10.1067\/mnc.2002.123122"},{"key":"2026010700553358000_12.2.e001616.10","doi-asserted-by":"publisher","DOI":"10.1093\/ehjci\/jev014"},{"key":"2026010700553358000_12.2.e001616.11","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1016\/j.echo.2019.08.012","article-title":"Similarities and Differences in Left Ventricular Size and Function among Races and Nationalities: Results of the World Alliance Societies of Echocardiography Normal Values Study","volume":"32","author":"Asch","year":"2019","journal-title":"J Am Soc Echocardiogr"},{"key":"2026010700553358000_12.2.e001616.12","doi-asserted-by":"crossref","unstructured":"Chen T , Guestrin C . 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