First, we developed a new tool, called SLECRISK, specifically designed to predict cardiovascular disease risk in systemic lupus erythematosus patients. This tool was created using machine learning. Machine learning teaches computers to learn information from data using statistics. Variables specific to systemic lupus erythematosus like disease activity, duration, and certain blood markers were included in the SLECRISK tool.

We studied patients who were 18 years of age or older, enrolled in the Brigham and Women’s Hospital systemic lupus erythematosus cohort. We collected data at the beginning of the study, including information such as traditional cardiovascular disease risk factors (e.g., blood pressure), patient demographics, and SLE-specific characteristics (e.g., serum creatinine). We followed patients for ten years to track the occurrence of major adverse cardiovascular events, such as heart attacks, strokes, or cardiac deaths. We used medical codes and had cardiologists confirm each event. We compared SLECRISK with existing tools like the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Risk Equations, Framingham Risk Score (FRS), and modified FRS (mFRS).