Accrual of Atherosclerotic Vascular Events in a Multicenter Inception Systemic Lupus Erythematosus Cohort
Bibliographic record
Abstract
Objective In previous studies, atherosclerotic vascular events (AVEs) were shown to occur in ~10% of patients with systemic lupus erythematosus (SLE). We undertook this study to investigate the annual occurrence and potential risk factors for AVEs in a multinational, multiethnic inception cohort of patients with SLE. Methods A large 33‐center cohort of SLE patients was followed up yearly between 1999 and 2017. AVEs were attributed to atherosclerosis based on SLE being inactive at the time of the AVE as well as typical atherosclerotic changes observed on imaging or pathology reports and/or evidence of atherosclerosis elsewhere. Analyses included descriptive statistics, rate of AVEs per 1,000 patient‐years, and univariable and multivariable relative risk regression models. Results Of the 1,848 patients enrolled in the cohort, 1,710 had ≥1 follow‐up visit after enrollment, for a total of 13,666 patient‐years. Of these 1,710 patients, 3.6% had ≥1 AVEs attributed to atherosclerosis, for an event rate of 4.6 per 1,000 patient‐years. In multivariable analyses, lower AVE rates were associated with antimalarial treatment (hazard ratio [HR] 0.54 [95% confidence interval (95% CI) 0.32–0.91]), while higher AVE rates were associated with any prior vascular event (HR 4.00 [95% CI 1.55–10.30]) and a body mass index of >40 kg/m2 (HR 2.74 [95% CI 1.04–7.18]). A prior AVE increased the risk of subsequent AVEs (HR 5.42 [95% CI 3.17–9.27], P < 0.001). Conclusion The prevalence of AVEs and the rate of AVE accrual demonstrated in the present study is much lower than that seen in previously published data. This may be related to better control of both the disease activity and classic risk factors.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".