Cardio-Rheumatology Insights Into Hypertension: Intersection of Inflammation, Arteries, and Heart
Bibliographic record
Abstract
There is an increased prevalence of atherosclerotic cardiovascular disease (ASCVD) in patients with inflammatory rheumatic diseases (IRD) including rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis, and systemic sclerosis. The mechanism for the development of ASCVD in these conditions has been linked not only to a higher prevalence and undertreatment of traditional cardiovascular (CV) risk factors but importantly to chronic inflammation and a dysregulated immune system which contribute to impaired endothelial and microvascular function, factors that may contribute to accelerated atherosclerosis. Accurate ASCVD risk stratification and optimal risk management remain challenging in this population with many barriers that include lack of validated risk calculators, the remitting and relapsing nature of underlying disease, deleterious effect of medications used to manage rheumatic diseases, multimorbidity, decreased mobility due to joint pain, and lack of clarity about who bears the responsibility of performing CV risk assessment and management (rheumatologist vs. primary care provider vs. cardiologist). Despite recent advances in this field, there remain significant gaps in knowledge regarding the best diagnostic and management approach. The evolving field of Cardio-Rheumatology focuses on optimization of cardiovascular care and research in this patient population through collaboration and coordination of care between rheumatologists, cardiologists, radiologists, and primary care providers. This review aims to provide an overview of current state of knowledge about ASCVD risk stratification in patients with IRD, contributing factors including effect of medications, and review of the current recommendations for cardiovascular risk management in patients with inflammatory disease with a focus on hypertension as a key risk factor.
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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) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".