Imaging-guided lipid-lowering therapy in rheumatology patients at cardiovascular risk
Bibliographic record
Abstract
Abstract Introduction Patients with rheumatological conditions have an increased risk of cardiovascular disease, yet traditional risk stratification tools may underestimate their atherosclerotic burden. Imaging modalities such as coronary computed tomography angiography (CCTA), coronary artery calcium (CAC) scoring, and carotid ultrasound may improve risk assessment and optimize lipid-lowering therapy (LLT). Purpose This study evaluates the role of imaging-guided lipid-lowering therapy (LLT) in rheumatology-referred patients, aiming to determine its impact on risk stratification, treatment modification, and clinical outcomes. Methods A retrospective cohort analysis was conducted on 121 patients referred by rheumatologists for cardiovascular risk assessment. Cardiovascular risk factors, lipid profiles, and ASCVD risk estimates were obtained. Patients underwent imaging based on an age- and symptom-stratified protocol: CCTA, CAC scoring, or carotid ultrasound. LLT was initiated or adjusted based on imaging findings, targeting an LDL goal of ≤70 mg/dL for patients with atherosclerosis and ≤130 mg/dL for those without. The primary endpoint was LDL reduction, and secondary outcomes included reclassification rates and cardiovascular event occurrence. Results Atherosclerosis was detected in 85 patients (70%), despite only 69 (57%) having an ASCVD risk ≥5% per standard calculators. Imaging led to reclassification in 25.6% of patients, resulting in LLT intensification in 42.4% of patients not indicated for treatment per AHA guidelines and de-escalation in 19.3% of those previously indicated for treatment. Post-treatment, LDL reduction was 35.9% in atherosclerotic patients, compared to 17.9% in non-atherosclerotic patients. Over a mean follow-up of 4.8 ± 1.4 years, no major cardiovascular events (myocardial infarction [MI], cerebrovascular accident [CVA], or unplanned revascularization) were observed, despite an expected event rate of 3.4%–7.6% based on five different risk estimation models. Conclusion Incorporating atherosclerosis imaging into routine evaluation for individuals with rheumatological conditions enhances risk stratification, allows for personalized treatment strategies, and was associated with a lower rate of cardiovascular events compared with what was predicted by traditional risk-based approaches.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".