Relative value of multiple plasma biomarkers as risk factors for coronary artery disease and death in an angiography cohort
Bibliographic record
Abstract
BACKGROUND: Although elevated levels of C-reactive protein (CRP), interleukin (IL)-6, serum amyloid A protein (SAA) and total homocysteine (tHcy) have been associated with the increased likelihood of cardiovascular events, the relative or combined utility of these biomarkers in predicting atherosclerosis and death in an angiography cohort is unknown. METHODS: A cohort of 1117 consecutive patients (797 men and 320 women), referred to 2 Vancouver teaching hospitals for selective coronary angiography, was recruited between 1993 and 1995. Angiography results were obtained for 1019 patients. In 2004 we determined that of 1050 patients who could be traced, 231 had died, 95 of CAD-related causes. We compared the relative utility of baseline measurements of CRP, IL-6, SAA and tHcy as well as of lipids for predicting angiographic CAD and all-cause and CAD-related death. RESULTS: The risk of death increased across quartiles for CRP, IL-6, SAA and tHcy. When comparing the highest and lowest quartiles, the greatest hazard ratios were associated with IL-6 (2.57, 95% confidence interval [CI] 1.62-4.09) and tHcy (2.36, 95% CI 1.53-3.65). A Cox regression model containing all plasma biomarkers and traditional risk factors indicated that age, angiographic CAD and baseline plasma levels of IL-6 and tHcy remained independent predictors of CAD-related death, whereas age, sex, smoking, diabetes and apolipoprotein B levels were independent predictors of angiographic CAD. Kaplan-Meier survival curves indicated a utility in combining measures of CRP, SAA, IL-6 and tHcy for predicting risk of all-cause and CAD-related death. INTERPRETATION: A comparison of elevated levels of CRP, IL-6, SAA and tHcy with traditional CAD risk factors indicated that IL-6 and tHcy were the strongest independent biomarkers for CAD-related death. Elevated levels of multiple biomarkers were associated with an increasing rate of all-cause and CAD-related death.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".