Related Experiment Video
Updated: May 5, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Machine learning-based cardiovascular risk prediction in systemic lupus erythematosus: development and internal
Noémie Benonnier1, Marie Robert1, Audrey Sousa Alves1
1Internal Medicine, Edouard Herriot Hospital, Hospices Civils de Lyon, Lyon, France.
Insights
Cardiovascular disease is a major risk in systemic lupus erythematosus (SLE). Antiphospholipid antibodies and skin involvement are key predictors, enabling a new risk assessment model for SLE patients.
Area of Science:
- Rheumatology
- Cardiology
- Data Science
Background:
- Cardiovascular diseases (CVD) are the primary cause of mortality in patients with systemic lupus erythematosus (SLE).
- Traditional cardiovascular risk factors are insufficient for assessing risk in SLE patients.
- SLE-specific determinants of cardiovascular risk require further investigation.
Purpose of the Study:
- To investigate cardiovascular outcomes in a cohort of SLE patients.
- To identify SLE-specific predictors of cardiovascular events (CVE).
- To develop a machine learning model for individualized cardiovascular risk assessment in SLE.
Main Methods:
- The LESLY cohort included SLE patients from January 2002 to August 2020.
- Cardiovascular events (CVE) were defined as myocardial ischemia or stroke.
- Multivariable analyses identified predictors, and machine learning models were developed using 80% training and 20% validation data.
Main Results:
- 55 SLE patients (6.3%) experienced CVE, including acute coronary syndromes and ischemic strokes.
- Antiphospholipid antibodies (HR=3.51) and inaugural skin involvement (HR=2.69) were significant predictors of CVE.
- An Elastic Net Penalized Cox model achieved accurate individualized CV risk prediction (C-index 0.791).
Conclusions:
- CVD is a primary concern in SLE, with antiphospholipid antibodies and skin involvement being key predictors.
- An exploratory risk stratification model was developed, potentially aiding clinical decision-making.
- External validation is required for the developed risk model.
Background:
Cardiovascular (CV) diseases are the leading cause of mortality in patients with systemic lupus erythematosus (SLE). While traditional risk factors inadequately assess CV risk, SLE-specific determinants remain elusive. We have conducted a study of CV outcomes in a cohort of SLE patients, aiming to facilitate individualized risk assessment.
Methods:
The LESLY cohort comprised patients diagnosed with SLE at the University Hospital of Lyon between January 2002 and August 2020. CV events (CVE) were defined as myocardial ischemia or stroke. Complete-case multivariable analyses identified predictors of CV risk, among baseline SLE characteristics and traditional CV factors. The identified predictors were subsequently employed to develop machine learning models estimating CV risk in patients with SLE. The dataset was partitioned into training (80%, n = 699) and validation (20%, n = 175) sets.
Results:
CVE occurred in 55 LESLY patients (6.3%), including 27 acute coronary syndromes and 25 ischemic strokes, over a mean follow-up of 8.8 ± 5.2 years. Antiphospholipid antibodies (HR = 3.51 [1.91-6.43], p < 0.001) and inaugural skin involvement (HR = 2.69 [1.25-5.77], p = 0.011) were the most potent predictors of CVE occurrence. Finally, an Elastic Net Penalized Cox model accurately predicted individualized CV risks in an internal validation cohort (C-index 0.791 [95% CI: 0.674-0.906]; Brier score 6.4% [95% CI: 3.7-9.8%]).
Conclusion:
This study corroborates the primacy of CVD in SLE, underscoring the predictive roles of inaugural skin involvement and antiphospholipid antibodies. These findings enabled the development of an exploratory risk stratification model that may inform clinical decision-making for cardiovascular risk management in SLE, pending external validation.