Disease Damage Influences Cardiovascular Risk Reclassification Based on Carotid Ultrasound in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Composite scores of cardiovascular (CV) risk factors underestimate the CV risk in patients with systemic lupus erythematosus (SLE). Carotid artery ultrasound (US) was found useful in identifying high CV-risk patients with inflammatory arthritis. We assessed the effect of carotid US assessments on the CV risk stratification of patients with SLE. METHODS: This cross-sectional study included 276 patients with SLE. These indices were measured: lipid profile, Systematic COronary Risk Evaluation (SCORE) risk calculation, and disease activity (SLE Disease Activity Index), severity (Katz), and damage [Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology Damage Index]. Carotid plaques were assessed by US. A multivariable regression analysis, adjusted for classic CV-related factors, was performed to evaluate how risk reclassification was influenced by disease characteristics in patients with SLE. RESULTS: Thirty-six percent of patients had carotid plaques. However, only 6% of them fulfilled the definitions for high or very high risk according to the SCORE risk charts. Following carotid US assessment, 32% of the patients were reclassified as very high risk. Disease duration (OR 1.04, 95% CI 1.00-1.07, p = 0.025) and a SLICC > 0 (OR 2.48 95% CI 1.15-5.34, p = 0.020) were independently associated with a higher risk of reclassification. A predictive model for reclassification included age (cutoff 52 yrs, sensitivity 60%, specificity 86%), disease duration (cutoff 24 yrs, sensitivity 40%, specificity 82%), presence of hypertension, SLICC > 0, waist circumference (cutoff 102 cm, sensitivity 48%, specificity 84%), and C3 (cutoff 127 mg/dl, sensitivity 52%, specificity 92%) and triglyceride (cutoff 140 mg/dl, sensitivity 68%, specificity 79%) serum levels. CONCLUSION: Reclassification into a very high-risk category is frequent after carotid US assessments in patients with SLE. This is independently influenced by disease damage.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".