Timing and Predictors of Incident Cardiovascular Disease in Systemic Lupus Erythematosus: Risk Occurs Early and Highlights Racial Disparities
Bibliographic record
Abstract
Objective Systemic lupus erythematosus (SLE) affects Black people 2 to 3 times more frequently than non-Black people and is associated with higher morbidity and mortality. In total, 4 studies with predominantly non-Black SLE cohorts highlighted that cardiovascular disease (CVD) is no longer primarily a late complication of SLE. This study assessed the timing and predictors of incident CVD in a predominantly Black population-based SLE cohort. Methods Incident SLE cases from the population-based Georgia Lupus Registry were validated as having a CVD event through review of medical records and matching with the Georgia Hospital Discharge Database and the National Death Index. The surveillance period for an incident CVD event spanned a 15-year period, starting from 2 years prior to SLE diagnosis. Results Among 336 people with SLE, 253 (75%) were Black and 56 (17%) had an incident CVD event. The frequency of CVD events peaked in years 2 and 11 after SLE diagnosis. There was a 7-fold higher risk of incident CVD over the entire 15-year period; this risk was 19-fold higher in the first 12 years in Black people as compared to non-Black people with SLE. Black people with SLE (P< 0.001) and those with discoid rash (hazard ratio 3.2, 95% CI 1.4-7.1) had a higher risk of incident CVD events. Conclusion The frequency of incident CVD events peaked in years 2 and 11 after SLE diagnosis. Being Black or having a discoid rash were strong predictors of an incident CVD event. Surveillance for CVD and preventive interventions, directed particularly toward Black people with recent SLE diagnoses, are needed to reduce racial disparities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".