Brief Report: The Ability of the 2013 American College of Cardiology/American Heart Association Cardiovascular Risk Score to Identify Rheumatoid Arthritis Patients With High Coronary Artery Calcification Scores
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) have increased risk of atherosclerotic cardiovascular disease that is underestimated by the Framingham Risk Score (FRS). We undertook this study to test the hypothesis that the 2013 American College of Cardiology/American Heart Association (ACC/AHA) 10-year risk score would perform better than the FRS and the Reynolds Risk Score (RRS) in identifying RA patients known to have elevated cardiovascular risk based on high coronary artery calcification (CAC) scores. METHODS: Among 98 RA patients eligible for risk stratification using the ACC/AHA risk score, we identified 34 patients with high CAC (defined as ≥300 Agatston units or ≥75th percentile of expected coronary artery calcium for age, sex, and ethnicity) and compared the ability of the 10-year FRS, RRS, and ACC/AHA risk scores to correctly assign these patients to an elevated risk category. RESULTS: All 3 risk scores were higher in patients with high CAC (P < 0.05). The percentage of patients with high CAC correctly assigned to the elevated risk category was similar among the 3 scores (FRS 32%, RRS 32%, ACC/AHA risk score 41%) (P = 0.223). The C statistics for the FRS, RRS, and ACC/AHA risk score predicting the presence of high CAC were 0.65, 0.66, and 0.65, respectively. CONCLUSION: The ACC/AHA 10-year risk score does not offer any advantage compared to the traditional FRS and RRS in the identification of RA patients with elevated risk as determined by high CAC. The ACC/AHA risk score assigned almost 60% of patients with high CAC to a low risk category. Risk scores and standard risk prediction models used in the general population do not adequately identify many RA patients with elevated cardiovascular risk.
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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 | 0.025 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".