Derivation and Internal Validation of an Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis: A Consortium of Rheumatology Researchers of North America Registry Study
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease (CVD) is the leading cause of mortality in rheumatoid arthritis (RA), but CV risk prediction scores derived from the general population do not accurately predict CV risk in RA patients. The goal of these analyses was to develop and internally validate an expanded CV risk prediction score for RA. METHODS: Study participants were patients with RA and no known CVD from the Consortium of Rheumatology Researchers of North America registry. Two-thirds of the cohort were used to derive the CV risk prediction score, and one-third for internal validation. Traditional CV risk factors were included in the base Cox regression model, and RA-related variables were assessed in an expanded model predicting confirmed CV events. Fit and utility of the expanded model were evaluated. RESULTS: The study cohort included 23,605 RA patients with 437 CV events over a median followup of 2.2 years. The RA variables found to be significant in the regression models and included in the expanded risk model were disease activity (Clinical Disease Activity Index >10 versus ≤10), disability (modified Health Assessment Questionnaire disability index >0.5 versus ≤0.5), daily prednisone use (any versus none), and disease duration (≥10 years versus <10 years). The expanded model had good fit (Hosmer-Lemeshow goodness of fit P = 0.94) and a lower Akaike's information criterion than the base model. In the internal validation cohort, the c-statistic for model discrimination was significantly improved from the base model to the expanded model (from 0.7261 to 0.7609; P = 0.0104). The net reclassification index of CV risk in models using a 4-category CV risk prediction tool was 40% (95% confidence interval 37-44%). CONCLUSION: This newly developed, expanded risk score for CV outcomes in RA performs well and improves the classification of CV risk in comparison to a risk prediction score in which only traditional risk factors were included.
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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 | 0.071 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".