O26 Incidence and predictors of atherosclerotic vascular events in a multicentre inception SLE cohort
Bibliographic record
Abstract
Background/Purpose The prevalence of atherosclerotic vascular events (AVE) in published literature of an inception cohort with SLE is 10%. We aimed to investigate the accrual and the associated factors of AVE in a multinational multiethnic inception cohort of patients with SLE. Methods A large 33-centre multinational inception cohort of SLE patients was followed yearly according to a standardized protocol between 1999–2017. AVEs are attributed to atherosclerosis on the basis of SLE being inactive at the time of the event, and the presence of typical atherosclerotic changes on imaging or pathology and/or evidence of atherosclerosis elsewhere. Analysis included descriptive statistics, rate of AVE’s per 1000 patient-years and univariable and multivariable relative risk regression models. Results Of the 1848 patients enrolled, 1710 that had at least one follow up visit after enrolment comprised of the study sample. 88.6% were female, 49.4% Caucasian, 16.4% Black, 15.0% Asian, 15.5% Hispanic and 3.7% other. Disease duration at enrolment was 5.7 ± 4.2 months, mean age at enrolment was 34.7± 13.4 years and SLEDAI-2K was 5.4 ± 5.4. The prevalence of AVEs was 3.6% and the rate per 1000-patient years was 4.6. Sixty-one patients had atherosclerotic events after the enrolment; their detailed events and numbers are listed in table 1. Two multivariable models including the predictors with significant effects in the single factor analyses, one without the aCL/LA variable and one with this variable are presented in table 1. The inclusion of aCL/LA led to the exclusion of 405 patients. Prior other nonatherosclerotic vascular events and high BMI were predictive of first AVE while only antimalarial therapy demonstrated a highly significant protective effect, [HR (95%CI): 0.54 (0.32, 0.91)], after adjustment for the other factors in the model. Conclusion More effective control of classic atherosclerotic risk factors and more frequent use of antimalarial may have both contributed to controlling AVEs in this inception cohort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".