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Updated: Jan 10, 2026
Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Predictive modeling of cardiovascular risk in systemic sclerosis: a single-center retrospective study integrating
Jingfeng Huang1, Le Yang2, Binhua Xie3
1Department of Radiology, The First Affiliated Hospital of Ningbo University, No.59 Liuting Street, Ningbo, 315000, China.
Insights
Systemic sclerosis (SSc) patients face high cardiovascular disease (CVD) risk. A new model using coronary artery calcium score (CACS), epicardial adipose tissue (EFV), modified Rodnan skin score (mRSS), and anti-Scl-70 antibody (ATA) accurately predicts CVD events.
Area of Science:
- Cardiology
- Rheumatology
- Medical Imaging
Background:
- Cardiovascular disease (CVD) is a primary cause of mortality in systemic sclerosis (SSc).
- Early identification of CVD risk is vital for improving patient outcomes in SSc.
Purpose of the Study:
- To develop a clinical prediction model for cardiovascular disease (CVD) risk in systemic sclerosis (SSc) patients.
- Integrate clinical and imaging data for enhanced CVD risk assessment in SSc.
Main Methods:
- Retrospective analysis of 245 SSc patients and 245 controls.
- Cox regression identified independent CVD risk predictors in SSc patients.
- Receiver operator characteristic (ROC) curves and Kaplan-Meier (KM) curves assessed model performance and survival associations.
Main Results:
- SSc patients had significantly higher CVD events than controls.
- Coronary artery calcium score (CACS), modified Rodnan skin score (mRSS), epicardial adipose tissue (EFV), and anti-Scl-70 antibody (ATA) were independent predictors of CVD risk.
- The combined model (CACS, EFV, mRSS, ATA) achieved an AUC of 0.910, demonstrating high predictive accuracy.
Conclusions:
- CACS, mRSS, EFV, and ATA are independent risk factors for CVD events in SSc.
- A predictive model integrating these factors demonstrates strong capability in assessing CVD incidence in SSc patients.
- The model shows high specificity and sensitivity for predicting cardiovascular events in this population.
Background:
Cardiovascular disease (CVD) is a leading cause of mortality in systemic sclerosis (SSc). Early risk identification is crucial for improving prognosis.
Objectives:
The aim of this study was to develop a clinical prediction model for assessing cardiovascular disease risk in SSc patients via integrating clinical and imaging data.
Methods:
We retrospectively analyzed the occurrence of CVD among 245 SSc patients and 245 controls. SSc patients were stratified by CVD event. Independent predictors of CVD risk were identified using Cox regression analysis in SSc patients. Receiver operator characteristic (ROC) curves assessed the model's predictive performance. Kaplan-Meier (KM) curves evaluated the association of these factors with event-free survival.
Results:
SSc patients exhibited significantly higher CVD events than controls. Significant differences were observed between SSc patients with and without events regarding age, sex, disease duration, modified Rodnan skin score (mRSS), erythrocyte sedimentation rate (ESR), anti-Scl-70 antibody (ATA), anti-U3 RNP, pulmonary arterial hypertension (PAH), interstitial lung disease (ILD), coronary artery calcium score (CACS) and epicardial adipose tissue (EFV). Cox regression identified CACS, mRSS, EFV, and ATA as independent predictors of increased CVD risk. The combined model (CACS, EFV, mRSS, ATA) achieved an area under the curve (AUC) of 0.910, showing high accuracy for predicting CVD events in SSc. KM analysis confirmed significantly reduced event-free survival in patients with high CACS, high mRSS, ATA positivity, or high EFV (all P < 0.05).
Conclusion:
CACS, mRSS, EFV, and ATA are independent risk factors for CVD events in SSc patients. A model combining these factors effectively predicts CVD incidence in this population. Key Points • CACS, mRSS, EFV, and ATA were identified as independent predictors of cardiovascular events. • The combination of CACS, EFV, mRSS, and ATA yielded a high AUC of 0.910, demonstrating strong predictive capability with high specificity and sensitivity for cardiovascular events in SSc individuals.
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