INCIDENT CARDIOVASCULAR AND VENOUS THROMBOEMBOLIC EVENTS IN AUTOANTIBODY-DEFINED SLE CLUSTERS
Bibliographic record
Abstract
PV197 / #398 Poster Topic: AS22 - SLE Heterogeneity Background/Purpose Patients with SLE are at high risk of cardiovascular (CV) and venous thromboembolic (VTE) events. Previously, we identified 4 autoantibody-defined SLE clusters,[1] associated with different HLA-DRB1 genotypes, clinical manifestations and cytokine patterns. We aimed to compare incidence rates of CV and VTE events between the clusters and to compare each cluster to a general population control group. Methods Unsupervised clustering, based on 13 autoantibodies, clustered SLE patients into 4 clusters: Cluster 1 was dominated (>50% positive) by anti-SSA/SSB, Cluster 2 by anti-nucleosome/Sm/RNP/dsDNA, Cluster 3 by aPL and Cluster 4 by negativity to the 13 autoantibodies. Information on vascular outcomes was collected through ICD codes from the National Patient Register. Controls from the Total Population Register were matched 10:1 on birth year, sex and residence to each patient. Subjects with a vascular event before enrollment were excluded. Incidence rates (IR) were calculated for 1000 person-years and 95% CI were calculated using the Poisson distribution. Age-adjusted Hazard Ratios (HR) and 95% CI from Cox proportional hazards models estimated relative risks of incident vascular events. Results 461 SLE patients were enrolled, mean follow-up of 12.24±5.6 years. SLE patients in cluster 2 were younger at inclusion and younger at first CV and VTE event. Cluster 3 had the highest crude IR for all the outcomes. The risk of major adverse CV events (MACE) in cluster 3 was almost twice as high compared to cluster 4 (Table 1). Moreover, cluster 3 had more than 2.5 higher risk of cerebrovascular events and VTE than cluster 1. Cluster 2 had similarly high HR for heart failure and VTE. Notably, HR for ischemic heart disease did not differ between cluster 4 and controls (Table 1). Table 1. Incidence rates and Hazard ratios (HR) for vascular outcomes between SLE clusters and between each cluster and general population controls. MACE: major adverse cardiovascular events; IHD:ischemic heart disease; VTE:venous thromboembolism; NP:not performed. *Age-adjusted Cox regression models. MACE= IHD+ischemic cerebrovascular events+peripheral arterial thrombosis/embolism+heart failure+death due to CV events; Cerebrovascular events=ischemic cerebrovascular events+cerebral hemorrhages; VTE=deep venous thrombosis+pulmonary embolism. Conclusions In SLE, incidence of MACE and VTE differs between autoantibody-defined clusters, with the highest incidence observed in the aPL positive and the lowest incidence in the autoantibody negative patients. References: [1.] Diaz-Gallo LM. ACR Open Rheum 2022;4(1):27-39.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".