通过综合机器学习分析增强SLE患者亚临床心功能障碍的预测。

IF 3.5 2区 医学 Q1 RHEUMATOLOGY
Yuhong Liu, Siwei Xie, Zhiming Lin, Changlin Zhao
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引用次数: 0

摘要

目的:探讨与SLE患者早期左心室收缩功能受损相关的二维斑点跟踪超声心动图(2D-STE)参数,并评估可能触发和影响左心室收缩功能受损的潜在临床因素。方法:本研究收集中山大学风湿病与免疫科2020年1月至2021年12月新诊断为SLE且系统性红斑狼疮疾病活动指数2000评分≥4分的患者36例。性别和年龄相匹配的健康对照也包括在内。所有参与者都进行了常规超声心动图和二维斑点跟踪超声心动图(2D-STE)检查。还收集了各种临床资料。使用机器学习和回归来估计SLE患者左心室收缩功能障碍的潜在危险因素。结论:本病例对照研究显示,2D-STE参数可用于预测SLE患者亚临床心功能障碍,抗u1rnp抗体可能是预测SLE患者亚临床心功能障碍的重要临床因素。机器学习可以进一步帮助初步筛选和量化SLE患者左心室收缩功能障碍的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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.

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来源期刊
Lupus Science & Medicine
Lupus Science & Medicine RHEUMATOLOGY-
CiteScore
5.30
自引率
7.70%
发文量
88
审稿时长
15 weeks
期刊介绍: Lupus Science & Medicine is a global, peer reviewed, open access online journal that provides a central point for publication of basic, clinical, translational, and epidemiological studies of all aspects of lupus and related diseases. It is the first lupus-specific open access journal in the world and was developed in response to the need for a barrier-free forum for publication of groundbreaking studies in lupus. The journal publishes research on lupus from fields including, but not limited to: rheumatology, dermatology, nephrology, immunology, pediatrics, cardiology, hepatology, pulmonology, obstetrics and gynecology, and psychiatry.
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