C-Reactive Protein as an Independent Cardiovascular Risk Predictor in HIV+ Patients: A Focused Review of Published Studies
Bibliographic record
Abstract
Patients infected with the human immunodeficiency virus (HIV+) are living longer and at heightened risk for developing cardiovascular events (CVEs). Commonly used prediction tools appear to misrepresent their CVE risk to varying degrees and in varying directions. Inclusion of markers of cellular infection, chronic immune activation and/or systemic inflammation into risk models might provide better predictive accuracy. Observational studies assessing the relationship of high-sensitivity C-reactive protein (hs-CRP) to CVE in HIV+ patients have reported inconsistent findings. This review of published studies attempted to determine if the available evidence supports its potential use in new models for stable, treated HIV+ patients. We searched the PubMed database using keywords and combinations of "HIV" AND "cardiovascular risk" AND "CRP". Papers presenting original analyses, associating hs-CRP concentration as an independent variable to hard cardiovascular outcomes (myocardial infarction and cardiovascular death), or to hard CVE as part of a composite endpoint, were included. Five observational studies met inclusion/exclusion criteria for review. Three papers identified an association between elevated hs-CRP and CVE, while two others failed to find any significant association. All reports were heterogeneous in terms of independent variables, controls, and designs. The larger and more rigorous studies, employing higher rates of confounder controls and more objective endpoints in their composites, showed positive associations. Though not conclusive, the preponderance of the evidence at this time supports CRP as a potentially valuable factor to be studied in prospective cardiovascular risk prediction investigations in HIV+ patients.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".