Role of Inflammation and High-Sensitivity C-reactive Protein in Atherosclerotic Cardiovascular Disease and CKD: A Survey of Nephrologists
Bibliographic record
Abstract
Background: In patients (pts.) with atherosclerotic cardiovascular disease (ASCVD) and chronic kidney disease (CKD), systemic inflammation (SI) contributes to increased cardiovascular (CV) risk. The FLAME-ASCVD-Nephro survey assessed awareness and perception of SI among nephrologists (nephs). Methods: An online observational study (NCT06322641) of nephs (Feb-April 2024) across 10 countries (Australia, Brazil, Canada, China, France, Germany, India, Italy, Japan, and Saudi Arabia) who treat ≥20 pts. with ASCVD+CKD a month and in practice for ≥3 years, was analyzed using descriptive statistics. Results: Of 3,778 participants, 294 completed the survey (~30/country). Traditional CV risk factors were most often discussed with pts.; SI to a lesser extent. 78% considered burden of SI higher in pts. with both ASCVD+CKD than CKD alone, and saw SI as an independent risk factor for ASCVD (66%) and linked to the development (63%) and progression of CKD (71%). 76% agreed SI as a contributor to the risk of recurrent CV events; 67% stated residual inflammatory risk persisted despite evidence-based preventive CV therapies. 7/10 reported testing SI to assist with clinical decisions, ie how aggressively to treat ASCVD (64%) and CKD (64%), and 83% used standard CRP. 74% would like to learn more about the role of SI in ASCVD and 35% agreed inclusion of high-sensitivity C-reactive protein (hsCRP) testing in guidelines would support clinical usage. Conclusion: This survey among nephs show SI was considered a CV risk factor, but not to the level of traditional CV risk factors. There is a need for medical education on the role of SI and guidance on hsCRP testing in pts. with ASCVD+CKD. Funding: Commercial Support - Novo Nordisk Health Care AG
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".